cycle_count#

The py_fatigue.py_fatigue module collects all functions related to cycle-counting(s) definition. The main class is CycleCount.

class py_fatigue.cycle_count.cycle_count.CycleCount(count_cycle: np.ndarray[Any, np.dtype[np.float64]], stress_range: np.ndarray[Any, np.dtype[np.float32]], mean_stress: np.ndarray[Any, np.dtype[np.float64]], timestamp: ADT | NDT = datetime.datetime(2024, 9, 5, 12, 37, 46, 241340, tzinfo=datetime.timezone.utc), unit: str = 'MPa', range_bin_lower_bound: float = 0.2, range_bin_width: float = 0.05, _mean_bin_lower_bound: float | None = None, mean_bin_width: float = 10, nr_small_cycles: float = 0, residuals_sequence: np.ndarray[Any, Any] = array([], dtype=float64), _min_max_sequence: np.ndarray[Any, Any] = array([], dtype=float64), lffd_solved: bool = False, mean_stress_corrected: str = 'No', stress_concentration_factor: float = 1.0, name: str | None = None, _time_sequence: np.ndarray | None = None)#

Cycle-counting class.

The class heavily relies on the py_fatigue.rainflow and py_fatigue.histogram modules.

CycleCount instances should be defined from either of the following two class methods:

from_rainflow()

from_timeseries()

classmethod from_rainflow(data: dict, timestamp: ADT | NDT = datetime.datetime(2024, 9, 5, 12, 37, 46, 241350, tzinfo=datetime.timezone.utc), round_decimals: int = 4, name: str | None = None, mean_stress_corrected: str = 'No', lffd_solved: bool = False, unit: str = 'MPa') → CycleCount#

Create a cycle-counting object from rainflow cycles. :param *args: Positional arguments passed to py_fatigue.rainflow.rainflow. :param **kwargs: Keyword arguments passed to py_fatigue.rainflow.rainflow.

Returns:: A cycle-counting object.
Return type:: CycleCount

classmethod from_timeseries(data: ndarray, time: ndarray | None = None, timestamp: ADT | NDT = datetime.datetime(2024, 9, 5, 12, 37, 46, 241352, tzinfo=datetime.timezone.utc), range_bin_lower_bound: float = 0.2, range_bin_width: float = 0.05, mean_bin_lower_bound: float | None = None, mean_bin_width: float = 10, name: str | None = None, unit: str = 'MPa') → CycleCount#

Generate a cycle-count from a timeseries.

Parameters:

*args – Positional arguments passed to _build_input_data_from_json.
**kwargs – Keyword arguments passed to _build_input_data_from_json.

property mean_bin_lower_bound: float#

The stress values.

Returns:: values
Return type:: np.ndarray

property time_sequence: ndarray#

The sequence of timestamps possibly added.

Returns:: time_sequence
Return type:: np.ndarray

property min_max_sequence: ndarray#

The minimum and maximum of the residuals (half-cycles) sequence.

Returns:: The minimum and maximum of the half-cycles sequence.
Return type:: np.ndarray, shape=(2, )

property residuals: ndarray#

Extracts the residuals, also known as half cycles, from mean-range couples of the cycle-counting object.

Returns:: Array containing mean stresses and stress ranges of the residuals. - residual_mean_stresses = array[:, 0] - residual_stress_ranges = array[:, 1]
Return type:: np.ndarray, shape (n, 2)

property full_cycles: ndarray#

Extracts the full cycles only from mean-range couples.

Returns:: Array containing mean stresses and stress ranges of the full cycles. - full_cycles_mean_stresses = array[:, 0] - full_cycles_stress_ranges = array[:, 1]
Return type:: np.ndarray, shape (n, 2)

property half_cycles: ndarray#: Symlink to residuals

See also

py_fatigue.cycle_count.CycleCount.residuals()

property mean_bin_upper_bound: float#

Returns the upper bound of the mean stress bin edges.

Returns:: The upper bound of the mean stress bins (highest mean bin edge).
Return type:: float

property range_bin_upper_bound: float#

Returns the upper bound of the stress range bins.

Returns:: The upper bound of the stress range bins (highest range bin edge).
Return type:: float

property bin_centers: tuple#

Extracts the bin centers from the cycle-counting object.

Returns:: Tuple containing mean stresses and stress ranges of the bin centers. - bin_centers_mean_stresses = tuple[0] - bin_centers_stress_ranges = tuple[1]
Return type:: tuple

property bin_edges: tuple#

Extracts the bin edges from the cycle-counting object.

Returns:: Tuple containing mean stresses and stress ranges of the bin edges. - bin_edges_mean_stresses = tuple[0] - bin_edges_stress_ranges = tuple[1]
Return type:: ht.tuple

property stress_amplitude: ndarray#

Extracts the stress amplitude from the cycle-counting object.

Returns:: Array containing the stress amplitude.
Return type:: np.ndarray

property min_stress: ndarray#

Extracts the min stress from the cycle-counting object.

Returns:: Array containing the min stresses.
Return type:: np.ndarray

property max_stress: ndarray#

Extracts the max stress from the cycle-counting object.

Returns:: Array containing the max stresses.
Return type:: np.ndarray

property statistical_moments: tuple[float, float, float]#

Calculate the spectral moments from the cycle-counting object.

Returns:: Tuple containing the spectral moments. - mean = tuple[0] - coefficient of variance = tuple[1] - skewness = tuple[2]
Return type:: tuple[float, float, float]

as_dict(damage_tolerance_for_binning: float = 0.001, damage_exponent: float = 5.0, round_decimals: int = 4, max_consecutive_zeros: int = 32, legacy_export: bool = False, debug_mode=False) → dict#

Convert the cycle-counting object to a dictionary.

Parameters:

damage_tolerance_for_binning (float, optional) – Tolerance for large-cycles calculation when binning, by default 0.001
damage_exponent (float, optional) – SN curve exponent used to define large cycles. Higher means more conservative, by default 5.0
max_consecutive_zeros (int, optional) – maximum number of consecutive zeros before saving a cycle to large cycles array. This quantity is introduced to save storage space, by default 8
round_decimals (int, optional) – Number of decimals after comma, by default 4

Returns:

Dictionary containing the following keys: - “hist” - “range_bin_lower_bound” - “range_bin_width” - “mean_bin_lower_bound” - “mean_bin_width” - “nr_small_cycles” - “res_sig” - “res” - “lg_c”

Return type:

dict