sn_curve#

The sn_curve module contains the SNCurve class and all the funcitons strictly related to its correct functioning. The function relies on the AbstractSNCurve class, which can be used as a abstract class for the definition of new SN curve models such as smart SN curves.

The SNCurve class#

class py_fatigue.SNCurve(slope: int | float | list | ndarray, intercept: int | float | list | ndarray, endurance: int | float = inf, environment: str | None = None, curve: str | None = None, norm: str | None = None, unit_string: str = 'MPa', color: str | None = None)#

Define an SN curve that can have an arbitrary number of slopes/intercepts and an endurance. For example:

>>> #
>>> #         ^                log N - log S
>>> # log(a1) +
>>> #         │*
>>> #         │ *
>>> #         │  *
>>> #         │   *  m1
>>> #         │    *---+
>>> # log(a2) +     *  |
>>> #         │  .   * | 1
>>> #     S   │     . *|
>>> #     t   │        *       m2
>>> #     r   │           *--------+
>>> #     e   │              *     | 1
>>> #     s   │                 *  |
>>> #     s   │                    *
>>> #         │                        *  *  *  *  *  *  *  *
>>> #         │
>>> #         │
>>> #         │────────────────────────|────────────────────────>
>>> #                                  Ne
>>> #                          Number of cycles

The slope-intercept couples ((m1, log_a1), (m2, log_a2)), and the endurance value (Ne) are the parameters necessary to fully describe this trilinear SN curve. If the endurance is not set, it defaults to Inf.

Example

>>> from py_fatigue import SNCurve
>>> import numpy as np

First we create an SN curve with the following properties:

>>> w3a = SNCurve(
>>>     [3, 5],
>>>     [10.970, 13.617],
>>>     norm='DNVGL-RP-C203',
>>>     curve='W3',
>>>     environment='Air',
>>> )

Then we can plot the SN curve defined using plotly

>>> data, layout = w3a.plotly()
>>> fig = go.Figure(data=data, layout=layout)
>>> fig.show

Define stress-life (SN) curve. See class docstring for more information.

Parameters:

  • slope (Union[int, float, list, np.ndarray]) – SN curve slope

  • intercept (Union[int, float, list, np.ndarray]) – Stress axis intercept

  • endurance (Union[int, float], optional) – endurance number of cycles, by default np.inf

  • environment (str, optional) – SN curve envirnoment, by default None

  • curve (str, optional) – SN curve category, by default None

  • norm (str, optional) – SN curve norm, by default None

  • unit_string (str, optional) – units, by default “MPa”

  • color (str, optional) – RGBS or HEX string for color, by default None

Public Data Attributes:

endurance_stress

Calculates the endurance stress, if endurance is defined.
Inherited from AbstractSNCurve

linear

Preventing attribute modification outside of constructor

slope

Preventing attribute modification outside of constructor

intercept

Preventing attribute modification outside of constructor

endurance

Preventing attribute modification outside of constructor

unit

Preventing attribute modification outside of constructor

endurance_stress

Calculates the endurance stress, if endurance is defined.

Public Methods:

format_name([html_format])

Reformat SNCurve name.

get_knee_stress([check_knee, significant_digits])

Return stress at the knee(s).

get_knee_cycles([check_knee, significant_digits])

Calculate the knee cycles.

get_cycles(stress_range)

Return cycles value(s) for stress range(s).

get_stress(cycles)

Return stress range(s) for the endurance(s) N.

n(sigma)

Equivalent of get_cycles().

sigma(n)

Equivalent of SNCurve.get_stress().

plotly([cycles, stress_range, dataset_name, ...])

Use plotly to plot the SN curve and a stress-cycles history dataset.

plot([cycles, stress_range, dataset_name, ...])

Use plotly to plot the SN curve and a stress-cycles history dataset.
Inherited from AbstractSNCurve

__init__(slope, intercept[, endurance, ...])

Define stress-life (SN) curve.

__str__()

SN curve __str__ method

__eq__(other)

Compare different SN curves and assert that they contain same attributes values

format_name([html_format])

Reformat SNCurve name.

get_knee_cycles([check_knee, significant_digits])

Calculate the knee cycles.

get_knee_stress([check_knee, significant_digits])

Return stress at the knee(s).

get_cycles(stress_range)

Return cycles value(s) for stress range(s).

get_stress(cycles)

Return stress range(s) for the endurance(s) N.

Private Data Attributes:

_abc_impl

Inherited from AbstractSNCurve

_abc_impl

Private Methods:

_repr_svg_()

SVG representation of the SN curve instance
Inherited from AbstractSNCurve

_repr_svg_()

SVG representation of the SN curve instance
_repr_svg_() → str#

SVG representation of the SN curve instance

Returns:

the SVG object

Return type:

str

property endurance_stress: float#

Calculates the endurance stress, if endurance is defined.

Returns:

endurance stress

Return type:

float

Raises:

ValueError – Endurance (XXX cycles) is not compatible with SN curve knees (list). Consider changing params

format_name(html_format: bool = False) → None#

Reformat SNCurve name.

Parameters:

html_format (bool, optional) – Choose whether the name format shall be HTML or not, by default True

get_knee_stress(check_knee: Collection | None = None, significant_digits: int = 2) → ndarray#

Return stress at the knee(s).

Parameters:

  • check_knee (int or float or list or 1darray, optional) – Cycles value(s) to check against, defaults to Inf

  • significant_digits (int, optional) – Assertion accuracy, defaults to 2

Returns:

Stress at the knee(s)

Return type:

1darray

get_knee_cycles(check_knee: Collection | None = None, significant_digits: int = 2) → ndarray#

Calculate the knee cycles.

Parameters:

  • check_knee (Collection, optional) – Collection of knee stress values, by default None

  • significant_digits (int, optional) – Number of significant digits, by default 2

Returns:

Knee cycles

Return type:

1darray

get_cycles(stress_range: int | float | list | ndarray) → float | ndarray#

Return cycles value(s) for stress range(s).

Parameters:

stress_range (int or float or list or 1darray) – stress range(s) to find the corresponding number of cycles value(s) for.

Returns:

number of cycles value(s) for the stress range(s) stress_range.

Return type:

float or 1darray

get_stress(cycles: int | float | list | ndarray) → float | ndarray#

Return stress range(s) for the endurance(s) N.

Parameters:

cycles (int or float or list or 1darray) – number of cycles value(s) to find the corresponding stress range(s) for.

Returns:

Stress range(s) for number of cycles value(s) cycles.

Return type:

float or 1darray

n(sigma: int | float | list | ndarray) → float | ndarray#

Equivalent of get_cycles(). Added for backwards compatibility. It will be removed in a future release.

Parameters:

sigma (int or float or list or 1darray) – stress range(s) to find the corresponding number of cycles value(s) for.

Returns:

number of cycles value(s) for the stress range(s) sigma.

Return type:

float or 1darray

sigma(n: int | float | list | ndarray) → float | ndarray#

Equivalent of SNCurve.get_stress(). Added for backwards compatibility. It will be removed in a future release

Parameters:

n (int or float or list or 1darray) – number of cycles value(s) to find the corresponding stress range(s) for.

Returns:

stress range(s) for the number of cycles value(s) n.

Return type:

float or 1darray

plotly(cycles: list | None = None, stress_range: list | None = None, dataset_name: str | None = None, dataset_color: str = '#000') → Tuple[list, dict]#

Use plotly to plot the SN curve and a stress-cycles history dataset.

Example

Use plotly to plot the SN curve

>>> data, layout = sncurve.plotly()
>>> fig = go.Figure(data=data, layout=layout)
>>> fig.show
Parameters:

  • cycles (list, optional) – number of cycles value(s) in the dataset, by default None

  • stress_range (list, optional) – Stress range(s) in the dataset, by default None

  • dataset_name (str, optional) – history dataset name, by default None

  • dataset_color (str, optional) – history dataset color, by default “#000”

Returns:

data, layout

Return type:

Tuple[list, dict]

plot(cycles: list | None = None, stress_range: list | None = None, dataset_name: str | None = None, dataset_color: str = '#000', fig: Figure | None = None, ax: Axes | None = None, **kwargs: Any) → Tuple[Figure, Axes]#

Use plotly to plot the SN curve and a stress-cycles history dataset.

Example

Use plotly to plot the SN curve

>>> fig, ax = sncurve.plot()
Parameters:

  • cycles (list, optional) – number of cycles value(s) in the dataset, by default None

  • stress_range (list, optional) – Stress range(s) in the dataset, by default None

  • dataset_name (str, optional) – history dataset name, by default None

  • dataset_color (str, optional) – history dataset color, by default “#000”

  • fig (matplotlib.figure.Figure, optional) – figure object, by default None

  • ax (matplotlib.axes.Axes, optional) – axis object, by default None

  • **kwargs (Any, optional) – additional keyword arguments

Returns:

The figure and axes.

Return type:

matplotlib.figure.Figure, matplotlib.axes.Axes