`ParsedLog`#

class pyedb.workflows.utilities.hfss_log_parser.ParsedLog#

Root container returned by HFSSLogParser.parse().

This class holds all parsed information from an HFSS log file and provides convenience methods for checking convergence, completion status, and extracting specific metrics.

Attributes:

projectProjectInfo: Project meta-data including name, file path, and design information.
init_meshInitMesh: Initial mesh statistics.
adaptivelist of AdaptivePass: Adaptive passes in chronological order.
sweepSweep or None: Frequency-sweep summary, or None if no sweep was performed.

Examples

>>> from pathlib import Path
>>> parsed = ParsedLog(
...     project=ProjectInfo(name="Test", file=Path("/tmp/test.aedt")),
...     init_mesh=InitMesh(tetrahedra=5000, memory_mb=100, real_time_sec=30, cpu_time_sec=28),
...     adaptive=[],
...     sweep=None,
... )
>>> parsed.project.name
'Test'

Overview#

Methods

`to_dict`	Deep-convert the entire object to JSON-serializable primitives.
`is_converged`	Check if the adaptive solver declared convergence.
`adaptive_passes`	Return the list of adaptive passes.
`memory_on_convergence`	Memory consumed by the last converged adaptive pass.
`is_completed`	Check if the simulation completed successfully.
`errors`	Extract error lines from the log file.

Attributes

`project`
`init_mesh`
`adaptive`
`sweep`

Import detail#

from pyedb.workflows.utilities.hfss_log_parser import ParsedLog

Attribute detail#

ParsedLog.project: ProjectInfo#

ParsedLog.init_mesh: InitMesh#

ParsedLog.adaptive: list[AdaptivePass]#

ParsedLog.sweep: Sweep | None#

Method detail#

ParsedLog.to_dict() → dict#

Deep-convert the entire object to JSON-serializable primitives.

Returns:

dict: Plain dict/list/scalar structure suitable for JSON serialization.

Examples

>>> parsed = ParsedLog(project=..., init_mesh=..., adaptive=[], sweep=None)
>>> data_dict = parsed.to_dict()
>>> isinstance(data_dict, dict)
True

ParsedLog.is_converged() → bool#

Check if the adaptive solver declared convergence.

Returns:

bool: True if at least one adaptive pass converged, False otherwise.

Examples

>>> parsed = ParsedLog(
...     project=ProjectInfo(name="T", file=Path("/t")),
...     init_mesh=InitMesh(tetrahedra=100, memory_mb=10, real_time_sec=5, cpu_time_sec=5),
...     adaptive=[
...         AdaptivePass(
...             pass_nr=1,
...             freq_hz=1e9,
...             tetrahedra=100,
...             matrix_size=50,
...             memory_mb=10,
...             delta_s=0.01,
...             converged=True,
...             elapsed_sec=10,
...         )
...     ],
...     sweep=None,
... )
>>> parsed.is_converged()
True

ParsedLog.adaptive_passes() → list[AdaptivePass]#

Return the list of adaptive passes.

Returns:

list of AdaptivePass: All adaptive passes in chronological order.

Examples

>>> parsed = ParsedLog(project=..., init_mesh=..., adaptive=[pass1, pass2], sweep=None)
>>> passes = parsed.adaptive_passes()
>>> len(passes)
2

ParsedLog.memory_on_convergence() → float#

Memory consumed by the last converged adaptive pass.

Returns:

float: Memory in megabytes, or math.nan if no pass converged.

Examples

>>> parsed = ParsedLog(
...     project=ProjectInfo(name="T", file=Path("/t")),
...     init_mesh=InitMesh(tetrahedra=100, memory_mb=10, real_time_sec=5, cpu_time_sec=5),
...     adaptive=[
...         AdaptivePass(
...             pass_nr=1,
...             freq_hz=1e9,
...             tetrahedra=100,
...             matrix_size=50,
...             memory_mb=256.5,
...             delta_s=0.01,
...             converged=True,
...             elapsed_sec=10,
...         )
...     ],
...     sweep=None,
... )
>>> parsed.memory_on_convergence()
256.5

ParsedLog.is_completed() → bool#

Check if the simulation completed successfully.

A simulation is considered complete when both adaptive convergence occurred and a frequency sweep was executed.

Returns:

bool: True if converged and sweep completed, False otherwise.

Examples

>>> parsed = ParsedLog(
...     project=ProjectInfo(name="T", file=Path("/t")),
...     init_mesh=InitMesh(tetrahedra=100, memory_mb=10, real_time_sec=5, cpu_time_sec=5),
...     adaptive=[
...         AdaptivePass(
...             pass_nr=1,
...             freq_hz=1e9,
...             tetrahedra=100,
...             matrix_size=50,
...             memory_mb=256,
...             delta_s=0.01,
...             converged=True,
...             elapsed_sec=10,
...         )
...     ],
...     sweep=Sweep(type="Interpolating", frequencies=11, solved=[1e9], elapsed_sec=30),
... )
>>> parsed.is_completed()
True

ParsedLog.errors() → list[str]#

Extract error lines from the log file.

Searches the log for lines containing error markers like [error] or *** ERROR ***. Warnings are ignored.

Returns:

list of str: List of stripped error lines, empty if none found.

Examples

>>> parsed = ParsedLog(project=..., init_mesh=..., adaptive=[], sweep=None)
>>> errs = parsed.errors()
>>> len(errs)
0