ross.ConvergenceResults
Contents
ross.ConvergenceResults#
- class ross.ConvergenceResults(el_num, eigv_arr, error_arr)#
Class used to store results and provide plots for Convergence Analysis.
- This class plots:
Natural Frequency vs Number of Elements Relative Error vs Number of Elements
- Parameters:
- el_numarray
Array with number of elements in each iteraction
- eigv_arrarray
Array with the n’th natural frequency in each iteraction
- error_arrarray
Array with the relative error in each iteraction
- Returns:
- figPlotly graph_objects.make_subplots()
The figure object with the plot.
Methods
- __init__(el_num, eigv_arr, error_arr)#
- classmethod load(file)#
Load results from a .toml file.
This function will load the simulation results from a .toml file. The file must have all the argument’s names and values that are needed to reinstantiate the class.
- Parameters:
- filestr, pathlib.Path
The name of the file the results will be loaded from.
Examples
>>> # Example running a stochastic unbalance response >>> from tempfile import tempdir >>> from pathlib import Path >>> import ross as rs >>> # Running an example >>> rotor = rs.rotor_example() >>> freq_range = np.linspace(0, 500, 31) >>> n = 3 >>> m = 0.01 >>> p = 0.0 >>> results = rotor.run_unbalance_response(n, m, p, freq_range) >>> # create path for a temporary file >>> file = Path(tempdir) / 'unb_resp.toml' >>> results.save(file) >>> # Loading file >>> results2 = rs.ForcedResponseResults.load(file) >>> abs(results2.forced_resp).all() == abs(results.forced_resp).all() True
- plot(fig=None, **kwargs)#
Plot convergence results.
- This method plots:
Natural Frequency vs Number of Elements Relative Error vs Number of Elements
- Parameters:
- figPlotly graph_objects.make_subplots()
The figure object with the plot.
- kwargsoptional
Additional key word arguments can be passed to change the plot layout only (e.g. width=1000, height=800, …). *See Plotly Python Figure Reference for more information.
- Returns:
- figPlotly graph_objects.make_subplots()
The figure object with the plot.
- classmethod read_toml_data(data)#
Read and parse data stored in a .toml file.
The data passed to this method needs to be according to the format saved in the .toml file by the .save() method.
- Parameters:
- datadict
Dictionary obtained from toml.load().
- Returns:
- The result object.
- save(file)#
Save results in a .toml file.
This function will save the simulation results to a .toml file. The file will have all the argument’s names and values that are needed to reinstantiate the class.
- Parameters:
- filestr, pathlib.Path
The name of the file the results will be saved in.
Examples
>>> # Example running a unbalance response >>> from tempfile import tempdir >>> from pathlib import Path >>> import ross as rs
>>> # Running an example >>> rotor = rs.rotor_example() >>> speed = np.linspace(0, 1000, 101) >>> response = rotor.run_unbalance_response(node=3, ... unbalance_magnitude=0.001, ... unbalance_phase=0.0, ... frequency=speed)
>>> # create path for a temporary file >>> file = Path(tempdir) / 'unb_resp.toml' >>> response.save(file)