vamseeachanta [!WARNING] Part of the Python API documented below does not exist in
digitalmodel. These snippets are a specification of intended capability, not runnable code. Do not import them, and do not report a result obtained by pretending they ran.Absent as of this revision:
digitalmodel.signal_processing.signal_analysis.orcaflex_signalsdigitalmodel.signal_processing.signal_analysis.orcaflex_signals.OrcaFlexSignalExtractordigitalmodel.signal_processing.signal_analysis.readersdigitalmodel.signal_processing.signal_analysis.readers.GenericTimeSeriesReaderdigitalmodel.signal_processing.signal_analysis.time_seriesdigitalmodel.signal_processing.signal_analysis.time_series.TimeSeriesProcessorThe surrounding engineering content — method, conventions, what to watch for — is unaffected and remains usable. Tracked in aceengineer-strategy#267.
Signal Analysis
When to Use
- Analyzing fatigue from stress/load time series
- Computing rainflow cycles for damage calculation
- FFT and power spectral density analysis
- Frequency spectrum characterization
- Batch processing OrcaFlex simulation signals
- Time series conditioning and filtering
- Converting time-domain data to frequency-domain
Prerequisites
- Python environment with
digitalmodelpackage installed - Time series data in CSV, Excel, or OrcaFlex format
- For OrcaFlex signals: completed .sim files
Python API
Rainflow Cycle Counting
from digitalmodel.signal_processing.signal_analysis.core.rainflow import RainflowCounter
# Initialize counter
counter = RainflowCounter()
# Load time history
import pandas as pd
data = pd.read_csv("stress_time_history.csv")
time = data["time"].values
*See sub-skills for full details.*
### Spectral Analysis
```python
from digitalmodel.signal_processing.signal_analysis.core.spectral import SpectralAnalyzer
import numpy as np
# Initialize analyzer
analyzer = SpectralAnalyzer()
# Load signal
data = pd.read_csv("motion_time_history.csv")
time = data["time"].values
*See sub-skills for full details.*
### Time Series Processing
```python
from digitalmodel.signal_processing.signal_analysis.time_series import TimeSeriesProcessor
# Initialize processor
processor = TimeSeriesProcessor()
# Load raw data
data = pd.read_csv("raw_signal.csv")
time = data["time"].values
signal = data["stress"].values
*See sub-skills for full details.*
### OrcaFlex Signal Extraction
```python
from digitalmodel.signal_processing.signal_analysis.orcaflex_signals import OrcaFlexSignalExtractor
from pathlib import Path
# Initialize extractor
extractor = OrcaFlexSignalExtractor()
# Extract time history from single .sim file
sim_file = Path("simulation.sim")
time, tension = extractor.extract_time_history(
*See sub-skills for full details.*
### Generic Time Series Reader
```python
from digitalmodel.signal_processing.signal_analysis.readers import GenericTimeSeriesReader
# Auto-detect file format and load
reader = GenericTimeSeriesReader()
# Read CSV
data = reader.read("data/measurements.csv")
# Read Excel
*See sub-skills for full details.*
## Related Skills
- [fatigue-analysis](../fatigue-analysis/SKILL.md) - Use rainflow cycles for fatigue damage calculation
- [orcaflex/post-processing](../orcaflex/post-processing/SKILL.md) - Extract time histories from OrcaFlex
- [structural-analysis](../structural-analysis/SKILL.md) - Stress analysis for signal generation
## References
- ASTM E1049-85: Standard Practices for Cycle Counting in Fatigue Analysis
- Welch, P.D. (1967): The Use of FFT for Estimation of Power Spectra
- DNV-RP-C203: Fatigue Design of Offshore Steel Structures
## Sub-Skills
- [Signal Quality (+2)](signal-quality/SKILL.md)
## Sub-Skills
- [Error Handling](error-handling/SKILL.md)
## Sub-Skills
- [Version Metadata](version-metadata/SKILL.md)
- [[1.0.0] - 2026-01-07](100-2026-01-07/SKILL.md)
- [1. Rainflow Cycle Counting (ASTM E1049-85) (+4)](1-rainflow-cycle-counting-astm-e1049-85/SKILL.md)
- [Complete Signal Analysis Workflow (+1)](complete-signal-analysis-workflow/SKILL.md)
- [Rainflow Cycles CSV (+2)](rainflow-cycles-csv/SKILL.md)