TensileTest API

TensileTest

Primary class for single tensile test analysis with automated processing pipeline. Follows a scikit-learn-like pattern with method chaining support.

from tensile import TensileTest

# Complete analysis pipeline
test = (TensileTest()
        .load("specimen.csv")
        .validate()
        .clean()
        .segment()
        .analyze())

# View results
print(test.summary())

# Create plot
fig = test.plot()
fig.show()

# Export results
test.export("results.csv")

Constructor

Initialize a TensileTest instance.

from tensile import TensileTest, TensileTestConfig

# With default configuration
test = TensileTest()

# With custom configuration
config = TensileTestConfig(jump_threshold=0.00001)
test = TensileTest(config=config)

Attributes

Methods

load()

Load CSV data from file with automatic column detection.

test = TensileTest()
test.load("data/specimen.csv")

# With custom delimiter
test.load("data/specimen.csv", delimiter=',')

validate()

Run validation checks on loaded data. Checks column existence, data quality, completeness, and physical reasonableness.

Updates: quality_report with validation results:

• is_valid: bool - Overall validity flag
• validation_issues: list - Specific problems found
• is_complete: bool - Whether test reached fracture
• completion_confidence: float - Confidence score (0-1)

test = TensileTest()
test.load("specimen.csv").validate()

if test.quality_report['is_valid']:
    print("Data is valid")
else:
    print("Issues:", test.quality_report['validation_issues'])

clean()

Execute cleaning pipeline to detect and correct measurement errors.

Processing steps:

1. Trim initial slack (pre-test data)
2. Detect strain jumps and slippages
3. Correct correctable slippages
4. Detect incomplete tests
5. Truncate post-fracture data

Updates:

• cleaned_data: DataFrame with corrected data
• quality_report['num_slippages_detected']: int
• quality_report['num_slippages_corrected']: int
• quality_report['slippage_details']: list of dicts

test.clean()
print(f"Slippages: {test.quality_report['num_slippages_detected']}")
print(f"Corrected: {test.quality_report['num_slippages_corrected']}")

segment()

Identify test phases using automated statistical segmentation. Finds elastic, plastic, and necking regions.

Updates: segments dictionary with keys:

• elastic_start: int - Start index of elastic region
• elastic_end: int - End index of elastic region
• yield_index: int - Approximate yield point
• plastic_start: int - Start of plastic deformation
• necking_start: int - Start of necking phase (if detected)

test.segment()
print(f"Elastic: {test.segments['elastic_start']} to {test.segments['elastic_end']}")

analyze()

Calculate all mechanical properties from stress-strain data.

Updates: results dictionary with calculated properties:

• youngs_modulus_GPa: float - Young's modulus (E)
• Rp02_MPa: float - 0.2% offset yield strength
• Rp02_strain: float - Strain at Rp0.2
• Rm_MPa: float - Ultimate tensile strength
• Rm_strain: float - Strain at maximum stress
• At_percent: float - Total elongation at fracture
• elastic_modulus_r_squared: float - R² of elastic fit
• analysis_successful: bool - Success flag

test.analyze()
print(f"E: {test.results['youngs_modulus_GPa']:.2f} GPa")
print(f"Rp0.2: {test.results['Rp02_MPa']:.2f} MPa")
print(f"Rm: {test.results['Rm_MPa']:.2f} MPa")
print(f"At: {test.results['At_percent']:.2f}%")

plot()

Create an interactive Plotly visualization of the stress-strain curve.

fig = test.plot()
fig.show()  # Display in browser
fig.write_html("plot.html")  # Save to file
fig.write_image("plot.png")  # Requires kaleido

export()

Export results to CSV file.

test.export("results.csv")
test.export("results_with_meta.csv", include_metadata=True)

summary()

Get formatted string summary of analysis results.

print(test.summary())

from_csv() [classmethod]

Create TensileTest instance directly from CSV file.

test = TensileTest.from_csv(
    "specimen.csv",
    metadata={'material': '316L', 'batch': 'A'}
)