Utilities Documentation

parameter docu

class helpr.utilities.parameter.Parameter(name, values, lower_bound=0, upper_bound=np.inf, size=False, dtype=float, error_function=ValueError)

Class to enable checking and enforcing parameter bounds.

static check_size(obj, size, dtype): Ensures object is of desired size.

static ensure_array(obj, size, dtype): Checks that object is an array and converts it otherwise.

static parameter_bounds_check(name, parameter_values, lower_bound, upper_bound, error_function): Checks that parameter values are within specified bounds.

helpr.utilities.parameter.divide_by_dataframe(numerator, denominator): Calculate division of a numpy array by a pandas DataFrame.

helpr.utilities.parameter.subtract_dataframe(minuend, subtrahend): Calculate subtraction of a numpy array by a pandas DataFrame.

plots docu

helpr.utilities.plots.ecdf(sample)

Calculates empirical distribution function for dataset.

Parameters:: sample – samples to be represented as an empirical cdf

helpr.utilities.plots.failure_assessment_diagram_equation(load_ratio): Calculates line from FAD equation.

helpr.utilities.plots.filter_failure_assessment_data(data): Filters out data for failure assessment diagram.

helpr.utilities.plots.generate_crack_growth_rate_plot(life_assessment, save_fig=False)

Creates a crack growth rate plot.

Parameters:

life_assessment (dict or DataFrame) – Single life assessment results.
save_fig (bool, optional) – Flag to save plot to a png file.

helpr.utilities.plots.generate_pipe_life_assessment_plot(life_assessment, life_criteria, pipe_name='', save_fig=False)

Generates deterministic plot life assessment plot.

Parameters:

life_assessment (dict or DataFrame) – Single pipe life assessment results.
life_criteria (dict or DataFrame) – Life criteria results.
pipe_name (str, optional) – Name of pipe to specify as title, defaults to no title.
save_fig (bool, optional) – Flag to save plot to a png file.

helpr.utilities.plots.plot_cycle_life_cdf_ci(analysis_results, criteria='Cycles to a(crit)')

Creates a plot of confidence intervals around cdfs of analysis results.

Parameters:

analysis_results (CrackEvolutionAnalysis) – Ensemble life assessment results.
criteria (str) – Life criteria to plot, defaults to ‘Cycles to a (crit)’.

helpr.utilities.plots.plot_cycle_life_cdfs(analysis_results, criteria='Cycles to a(crit)', save_fig=False)

Creates a plot with cdfs of analysis results.

Parameters:

analysis_results (CrackEvolutionAnalysis) – Ensemble life assessment results.
criteria (str) – Life criteria to plot, defaults to ‘Cycles to a (crit)’.
save_fig (bool, optional) – Flag to save plot to a png file.

helpr.utilities.plots.plot_cycle_life_criteria_scatter(analysis_results, criteria='Cycles to a(crit)', color_by_variable=False, save_fig=False)

Creates scatter plots of cycle life QOI results. If save_fig is True, will return filepath str (if not color_by_variable) or list of filepaths if colored by variable.

Parameters:

analysis_results (CrackEvolutionAnalysis) – Ensemble life assessment results.
criteria (str, optional) – Life criteria to plot, defaults to ‘Cycles to a (crit)’.
color_by_variable (bool, optional) – Flag to change colors by variable.
save_fig (bool, optional) – Flag to save plot to a png file.

helpr.utilities.plots.plot_cycle_life_pdfs(analysis_results, criteria='Cycles to a(crit)', save_fig=False)

Creates pdfs of life cycle analysis results.

Parameters:

analysis_results (CrackEvolutionAnalysis) – Ensemble life assessment results.
criteria (str) – Life criteria to plot, defaults to ‘Cycles to a (crit)’.
save_fig (bool, optional) – Flag to save plot to a png file.

helpr.utilities.plots.plot_det_design_curve(dk, da_dn, save_fig=False)

Creates a plot of design curve values exercised in an analysis.

Parameters:

dk (pandas.DataFrame) – Change in stress intensity factor.
da_dn (pandas.DataFrame) – Change of crack size over change in cycles (da/dn).
save_fig (bool, optional) – Flag to save plot to a png file.

helpr.utilities.plots.plot_failure_assessment_diagram(life_assessment, nominal=False, save_fig=False)

Creates a failure assessment diagram (FAD).

Parameters:

life_assessment (dict) – Single or Ensemble life assessment results.
nominal (bool, optional) – Flag for nominal or probabilistic results.
save_fig (bool, optional) – Flag to save plot to a png file.

helpr.utilities.plots.plot_log_hist(data, label, logbins=None)

Create a log10-scale histogram of the given data.

Parameters:

data (np.ndarray) – Data to bin and plot.
label (str) – Legend label for data.
logbins (np.ndarray, optional) – Option to pass in log spaced bins if already computed.

Returns:

logbins – Bin locations in log10 spacing.

Return type:

np.array

helpr.utilities.plots.plot_mitigation_histograms(analysis_results, mitigated, inspection_interval)

Create histogram plots showing cracks failing over time and the impact of inspection.

Parameters:

analysis_results (dict) – Life criteria data from fatigue analysis.
mitigated (list) – Indication of which cracks were mitigated through inspection.
inspection_interval (float) – Frequency of inspections.

helpr.utilities.plots.plot_pipe_life_ensemble(life_assessment, criteria='Cycles to a(crit)', save_fig=False)

Creates plot of ensemble pipe life assessment results.

Parameters:

life_assessment (CrackEvolutionAnalysis) – Ensemble life assessment results.
life_criteria (dict) – Life criteria results.
save_fig (bool, optional) – Flag to save plot to a png file.

helpr.utilities.plots.plot_sensitivity_results(analysis_results, criteria='Cycles to a(crit)', save_fig=False)

Creates a plot of sensitivity results.

Parameters:

analysis_results (CrackEvolutionAnalysis) – Ensemble life assessment results.
criteria (str, optional) – Life criteria to plot, defaults to ‘Cycles to a (crit)’.
save_fig (bool, optional) – Flag to save plot to a png file.

helpr.utilities.plots.plot_unscaled_mitigation_cdf(analysis_results, mitigated, inspection_interval)

Creates a plot of unscaled cdfs showing impact of inspection/mitigation.

Parameters:

analysis_results (dict) – Life criteria data from fatigue analysis.
mitigated (list) – Indication of which cracks were mitigated through inspection.
inspection_interval (float) – Frequency of inspections.

postprocessing docu

helpr.utilities.postprocessing.calc_a_over_t_criterion_0(pipe_specification, a_crit): Calculates ‘a critical’ life criteria.

helpr.utilities.postprocessing.calc_a_over_t_criterion_1(pipe_specification, a_crit): Calculates ‘25% a critical’ life criteria.

helpr.utilities.postprocessing.calc_a_over_t_criterion_2(cycle_sheet, cycles_to_half_a_crit_cycles): Calculates ‘half a critical cycles’ life criteria.

helpr.utilities.postprocessing.calc_pipe_life_criteria(cycle_results, pipe, stress_state)

Calculates overall pipe life criteria.

Parameters:

cycle_results (dict) – Complete load cycling results.
pipe_index (int) – Index of single pipe instance.
stress_state (GenericStressState) – Stress state specification.

Returns:

life_criteria – Collected life criteria results.

Return type:

dict

helpr.utilities.postprocessing.calculate_failure_assessment(parameters, fatigue_results, stress_state)

Calculates failure assessment values for load cycling results.

Parameters:

parameters (dict) – Analysis input parameters.
fatigue_results (dict) – Analysis load cycling results.
stress_state (GenericStressState) – Stress state specification.

helpr.utilities.postprocessing.parallel_interpolation_list_pts(interpolation_points, x_vals, y_vals)

Interpolates a list of points in parallel.

Parameters:

interpolation_points (list) – List of a critical values.
x_vals (pandas.Series) – Series of x values for interpolated data.
y_vals (pandas.Series) – Series of y values for interpolated data.

Returns:

interpolation_results

Return type:

numpy.ndarray

helpr.utilities.postprocessing.parallel_interpolation_single_pt(interpolation_points, x_vals, y_vals)

Interpolates single points in parallel.

Parameters:

interpolation_points (list) – List of a critical values.
x_vals (pandas.Series) – Series of x values for interpolated data.
y_vals (pandas.Series) – Series of y values for interpolated data.

Returns:

interpolation_results

Return type:

numpy.ndarray

helpr.utilities.postprocessing.report_single_cycle_evolution(all_results, pipe_index)

Creates a DataFrame object for plotting a single crack cycle evolution.

Parameters:

all_results (dict) – All load cycling results.
pipe_index (int) – Index of single pipe instance.

Returns:

single_cycle_evolution – DataFrame for plotting of single instance.

Return type:

pd.DataFrame

helpr.utilities.postprocessing.report_single_pipe_life_criteria_results(life_results, pipe_index)

Reports pipe life criteria results for single instance.

Parameters:

life_results (dict) – Complete load cycling results.
pipe_index (int) – Index of single pipe instance.

Returns:

pipe_life – Collected life criteria results.

Return type:

pandas.DataFrame

unit_conversion docu

Common unit conversion functions.

helpr.utilities.unit_conversion.convert_in_to_m(inch_value: float) → float: Converts inches to meters.

helpr.utilities.unit_conversion.convert_ksi_to_mpa(ksi_value: float) → float: Converts KSI to MPa.

helpr.utilities.unit_conversion.convert_psi_to_mpa(psi_value: float) → float: Converts PSI to MPa.

helpr.utilities.unit_conversion.get_variable_units(variable_name: str, for_plotting: bool = True) → str: Specifies units used for analysis variables