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)