sdynpy.ShapeArray

• Defined as: sdynpy.core.sdynpy_shape.ShapeArray

• Module: sdynpy.core.sdynpy_shape

• Source: GitHub

• Parent: sdynpy.SdynpyArray

• Parent: numpy.ndarray

Signature¶

class sdynpy.ShapeArray(shape, ndof, shape_type='real', buffer=None, offset=0, strides=None, order=None)

Shape information specifying displacements at nodes.

Use the shape_array helper function to create the array.

Attributes¶

modeshape¶

The mode shape matrix with degrees of freedom as second to last axis

ndof¶

The number of degrees of freedom in the shape

Methods¶

complex_data_dtype¶

• Source: GitHub

def sdynpy.ShapeArray.complex_data_dtype(ndof)

Data type of the underlying numpy structured array for complex shapes

Parameters¶

• ndof : int Number of degrees of freedom in the shape array

Returns¶

• list

• Numpy dtype that can be passed into any of the numpy array

• constructors

compute_frf¶

• Source: GitHub

def sdynpy.ShapeArray.compute_frf(self, frequencies, responses=None, references=None, displacement_derivative=2)

Computes FRFs from shape data

Parameters¶

• frequencies : iterable A list of frequencies to compute the FRF at.

• responses : CoordinateArray, optional Degrees of freedom to use as responses. The default is to compute FRFs at all degrees of freedom in the shape.

• references : CoordinateArray, optional Degrees of freedom to use as references. The default is to compute FRFs using the response degrees of freedom also as references.

• displacement_derivative : int, optional The derivative to use when computing the FRFs. 0 corresponds to displacement FRFs, 1 corresponds to velocity, and 2 corresponds to acceleration. The default is 2.

Returns¶

• output_data : TransferFunctionArray A transfer function array containing the specified references and responses.

concatenate_dofs¶

• Source: GitHub

def sdynpy.ShapeArray.concatenate_dofs(shape_arrays)

Combines the degrees of freedom from multiple shapes into one set of shapes

Parameters¶

• shape_arrays : list of ShapeArray List of ShapeArray objects to combine in to one set of shapes

Returns¶

• ShapeArray

• ShapeArray object containing degrees of freedom from all input shapes

edit_comments¶

• Source: GitHub

def sdynpy.ShapeArray.edit_comments(self, geometry=None)

Opens up a table where the shape comments can be edited

If a geometry is also passed, it will also open up a mode shape plotter window where you can visualize the modes you are looking at.

Edited comments will be stored back into the ShapeArray object when the OK button is pressed. Comments will not be stored if the Cancel button is pressed.

Parameters¶

• geometry : Geometry, optional A geometry on which the shapes will be plotted. If not specified, a table will just open up.

Returns¶

• ShapeCommentTable

• A ShapeCommentTable displaying the modal information where comments

• can be edited.

Notes¶

Due to how Python handles garbage collection, the table may be immediately closed if not assigned to a variable, as Python things it is no longer in use.

expand¶

• Source: GitHub

def sdynpy.ShapeArray.expand(self, initial_geometry, expansion_geometry, expansion_shapes, node_id_map=None, expansion_coordinates=None, return_coefficients=False)

Perform SEREP expansion on shape data

Parameters¶

• initial_geometry : Geometry The initial or “Test” Geometry, corresponding to the initial shapes (self)

• expansion_geometry : Geometry The expanded or “FEM” Geometry, corresponding to the shapes that will be expanded

• expansion_shapes : ShapeArray Shapes defined on the expanded geometry, which will be used to expand the initial shapes

• node_id_map : id_map, optional If the initial and expanded geometry or shapes do not have common node ids, an id_map can be specified to map the finite element node ids to test node ids. The default is None, which means no mapping will occur, and the shapes have common id numbers.

• expansion_coordinates : CoordinateArray, optional Degrees of freedom in the test shapes to use in the expansion. The default is None, which results in all degrees of freedom being used for expansion

• return_coefficients : bool, optional If True, the coefficients used in the expansion will be returned along with the expanded shapes. The default is False.

Returns¶

• ShapeArray

• The original shapes expanded to the expansion_geometry

• np.ndarray

• The coefficients used to perform the expansion, only returned if

• return_coefficients is True

from_exodus¶

• Source: GitHub

def sdynpy.ShapeArray.from_exodus(cls, exo, x_disp='DispX', y_disp='DispY', z_disp='DispZ', x_rot=None, y_rot=None, z_rot=None, timesteps=None)

Reads shape data from displacements in an Exodus file

Parameters¶

• exo : Exodus or ExodusInMemory The exodus data from which shapes will be created.

• x_disp : str, optional String denoting the nodal variable in the exodus file from which the X-direction displacement should be read. The default is ‘DispX’. Specify None if no x_disp is to be read.

• y_disp : str, optional String denoting the nodal variable in the exodus file from which the Y-direction displacement should be read. The default is ‘DispY’. Specify None if no y_disp is to be read.

• z_disp : str, optional String denoting the nodal variable in the exodus file from which the Z-direction displacement should be read. The default is ‘DispZ’. Specify None if no z_disp is to be read.

• x_rot : str, optional String denoting the nodal variable in the exodus file from which the X-direction rotation should be read. The default is None which results in the X-direction rotation not being read. Typically this would be set to ‘RotX’ if rotational values are desired.

• y_rot : str, optional String denoting the nodal variable in the exodus file from which the Y-direction rotation should be read. The default is None which results in the Y-direction rotation not being read. Typically this would be set to ‘RotY’ if rotational values are desired.

• z_rot : str, optional String denoting the nodal variable in the exodus file from which the Z-direction rotation should be read. The default is None which results in the Z-direction rotation not being read. Typically this would be set to ‘RotZ’ if rotational values are desired.

• timesteps : iterable, optional A list of timesteps from which data should be read. The default is None, which reads all timesteps.

Returns¶

• ShapeArray

• Shape data from the exodus file

from_imat_struct¶

• Source: GitHub

def sdynpy.ShapeArray.from_imat_struct(cls, imat_shp_struct)

Constructs a ShapeArray from an imat_shp class saved to a Matlab structure

In IMAT, a structure can be created from an imat_shp by using the get() function. This can then be saved to a .mat file and loaded using scipy.io.loadmat. The output from loadmat can be passed into this function

Parameters¶

• imat_fem_struct : np.ndarray structure from loadmat containing data from an imat_shp

Returns¶

• ShapeArray

• ShapeArray constructed from the data in the imat structure

from_uff¶

• Source: GitHub

def sdynpy.ShapeArray.from_uff(unv_data_dict, combine=True)

Load ShapeArrays from universal file data from read_unv

Parameters¶

• unv_data_dict : dict Dictionary containing data from read_unv

• combine : bool, optional If True, return as a single ShapeArray

Returns¶

• output_arrays : ShapeArray Shapes read from unv

Raises¶

• ValueError

• Raised if an unknown data characteristic is provided

from_unv¶

• Source: GitHub

def sdynpy.ShapeArray.from_unv(unv_data_dict, combine=True)

Load ShapeArrays from universal file data from read_unv

Parameters¶

• unv_data_dict : dict Dictionary containing data from read_unv

• combine : bool, optional If True, return as a single ShapeArray

Returns¶

• output_arrays : ShapeArray Shapes read from unv

Raises¶

• ValueError

• Raised if an unknown data characteristic is provided

is_complex¶

• Source: GitHub

def sdynpy.ShapeArray.is_complex(self)

Returns true if the shape is a complex shape, False if shape is real

Returns¶

• bool

• True if the shape is complex

mac¶

• Source: GitHub

def sdynpy.ShapeArray.mac(self)

Computes the modal assurance criterion matrix of the shapes

Returns¶

• mac

• The modal assurance criterion matrix

mode_table¶

• Source: GitHub

def sdynpy.ShapeArray.mode_table(self, table_format='csv', frequency_format='{:0.2f}', damping_format='{:0.2f}%')

Generates a table of modal information including frequency and damping

Parameters¶

• table_format : str, optional The type of table to generate. Can be ‘csv’, ‘rst’, ‘markdown’, ‘latex’, ‘pandas’, or ‘ascii’. The default is ‘csv’.

• frequency_format : str, optional Format specifier for frequency. The default is ‘{:0.2f}’.

• damping_format : str, optional Format specifier for damping percent. The default is ‘{:0.2f}%’.

Returns¶

• table : str String representation of the mode table

Raises¶

• ValueError

• Raised if a invalid table_format is specified

normalize¶

• Source: GitHub

def sdynpy.ShapeArray.normalize(self, system_or_matrix, return_modal_matrix=False)

Computes A-normalized or mass-normalized shapes

Parameters¶

• system_or_matrix : System or np.ndarray A System object or a mass matrix for real modes or A-matrix for complex modes.

• return_modal_matrix : bool, optional If true, it will return the modal mass or modal-A matrix computed from the normalized mode shapes. The default is False.

Returns¶

• ShapeArray

• A copy of the original shape array with normalized shape coefficients

optimize_degrees_of_freedom¶

• Source: GitHub

def sdynpy.ShapeArray.optimize_degrees_of_freedom(self, sensors_to_keep, group_by_node=False, method='ei')

Creates a reduced set of shapes using optimal degrees of freedom

Parameters¶

• sensors_to_keep : int Number of sensors to keep

• group_by_node : bool, optional If True, group shape degrees of freedom a the same node as one sensor, like a triaxial accelerometer. The default is False.

• method : str, optional ‘ei’ for effective independence or ‘cond’ for condition number. The default is ‘ei’.

Returns¶

• ShapeArray

• The set of shapes with a reduced set of degrees of freedom that are

• optimally chosen.

overlay_shapes¶

• Source: GitHub

def sdynpy.ShapeArray.overlay_shapes(geometries, shapes, color_override=None)

Combines several shapes and geometries for comparitive visualization

Parameters¶

• geometries : Iterable of Geometry A list of Geometry objects that will be combined into a single Geometry

• shapes : Iterable of ShapeArray A list of ShapeArray objects that will be combined into a single ShapeArray

• color_override : iterable, optional An iterble of integers specifying colors, which will override the existing geometry colors. This should have the same length as the geometries input. The default is None, which keeps the original geometry colors.

Returns¶

• new_geometry : Geometry A geometry consisting of a combination of the specified geometries

• new_shape : ShapeArray A ShapeArray consisting of a combination of the specified ShapeArrays

plot_frequency¶

• Source: GitHub

def sdynpy.ShapeArray.plot_frequency(self, interp_abscissa, interp_ordinate, ax=None, plot_kwargs={'color': 'k', 'marker': 'x', 'linestyle': 'None'})

Plots the frequencies of the shapes on curves of a 2D plot

Parameters¶

• interp_abscissa : np.ndarray The abscissa used to interpolate the Y-position of the frequency mark

• interp_ordinate : np.ndarray The ordinate used to interpolate the Y-postiion of the frequency mark

• ax : matplotlib axes, optional Axes on which to plot the frequency marks. The default is None, which creates a new window

• plot_kwargs : dict, optional Additional arguments to pass to the matplotlib plot command. The default is {‘color’:‘k’,‘marker’:‘x’,‘linestyle’:“None”}.

Returns¶

• ax : matplotlib axes, optional Axes on which the frequency marks were plotted

plot_mac¶

• Source: GitHub

def sdynpy.ShapeArray.plot_mac(self, *args, **kwargs)

Plots the mac matrix of the shapes

real_data_dtype¶

• Source: GitHub

def sdynpy.ShapeArray.real_data_dtype(ndof)

Data type of the underlying numpy structured array for real shapes

Parameters¶

• ndof : int Number of degrees of freedom in the shape array

Returns¶

• list

• Numpy dtype that can be passed into any of the numpy array

• constructors

reduce¶

• Source: GitHub

def sdynpy.ShapeArray.reduce(self, nodelist_or_coordinate_array)

Reduces the shape to the degrees of freedom specified

Parameters¶

• nodelist_or_coordinate_array : iterable or CoordinateArray Consists of either a list of node ids or a CoordinateArray. The ShapeArray will be reduced to only the degrees of freedom specified

Returns¶

• ShapeArray

• A reduced ShapeArray

reduce_for_comparison¶

• Source: GitHub

def sdynpy.ShapeArray.reduce_for_comparison(self, comparison_shape, node_id_map=None)

repack¶

• Source: GitHub

def sdynpy.ShapeArray.repack(self, coefficients)

Creates new shapes by linearly combining existing shapes

Parameters¶

• coefficients : np.ndarray coefficient matrix that will be multiplied by the shapes. This should have number of rows equal to the number of shapes that are going to be combined. The number of columns will specify the number of shapes that will result

Returns¶

• ShapeArray

• ShapeArray consisting of linear combinations of the original

• ShapeArray

shape_alignment¶

• Source: GitHub

def sdynpy.ShapeArray.shape_alignment(shape_1, shape_2, node_id_map=None)

Computes if the shapes are aligned, or if one needs to be flipped

Parameters¶

• shape_1 : ShapeArray Shape to compare.

• shape_2 : ShapeArray Shape to compare.

Returns¶

• np.ndarray

• An array denoting if one of the shapes need to be flipped (-1)

• to be equivalent to the other, or if they are already aligned (1)

system¶

• Source: GitHub

def sdynpy.ShapeArray.system(self)

Create system matrices from the shapes

This will create a System object with modal mass, stiffness, and damping matrices, with the mode shape matrix as the transformation to physical coordinates

Returns¶

• System

• A System object containing modal mass, stiffness, and damping

• matrices, with the mode shape matrix as the transformation

• to physical coordinates

Raises¶

• NotImplementedError

• Raised if complex modes are used

to_complex¶

• Source: GitHub

def sdynpy.ShapeArray.to_complex(self)

Creates complex shapes from real shapes

Returns¶

• ShapeArray

• Complex shapes compute from the real shape coefficients

to_real¶

• Source: GitHub

def sdynpy.ShapeArray.to_real(self, force_angle=-0.7853981633974483, **kwargs)

Creates real shapes from complex shapes by collapsing the complexity

Parameters¶

• force_angle : float Angle to force the complex collapsing to real, by default it is -pi/4 (-45 degrees). To allow other angles, use None for this argument.

• **kwargs : various Extra arguments to pass to collapse_complex_to_real

Returns¶

• ShapeArray

• Real shapes created from the original complex shapes

transform_coordinate_system¶

• Source: GitHub

def sdynpy.ShapeArray.transform_coordinate_system(self, original_geometry, new_geometry, node_id_map=None, rotations=False, missing_dofs_are_zero=False)

Performs coordinate system transformations on the shape

Parameters¶

• original_geometry : Geometry The Geometry in which the shapes are currently defined

• new_geometry : Geometry The Geometry in which the shapes are desired to be defined

• node_id_map : id_map, optional If the original and new geometries do not have common node ids, an id_map can be specified to map the original geometry node ids to new geometry node ids. The default is None, which means no mapping will occur, and the geometries have common id numbers.

• rotations : bool, optional If True, also transform rotational degrees of freedom. The default is False.

• missing_dofs_are_zero : bool, optional If False, any degree of freedom required for the transformation that is not provided will result in a ValueError. If True, these missing degrees of freedom will simply be appended to the original shape matrix as zeros. Default is False.

Returns¶

• ShapeArray

• A ShapeArray that can now be plotted with the new geometry

transformation_matrix¶

• Source: GitHub

def sdynpy.ShapeArray.transformation_matrix(self, physical_coordinates, inversion=True, normalized=True)

Creates a transformation matrix that describes a transformation from a physical coordinate array into modal space using the provided mode shapes.

Parameters¶

• coordinates : CoordinateArray The “physical” force coordinates for the transformation.

• inversion : bool, optional If True, the pseudo inverse of the mode shape matrix will be performed. If False, the mode shape matrix will not be inverted.

• normalized : bool, optional If True, the rows of the transformation matrix will be normalized to unit magnitude.

Returns¶

• transformation : Matrix The transformation matrix as a matrix object. It is organized with the modal CoordinateArray on the rows and the physical force CoordinateArray on the columns.

Notes¶

The transformation automatically handles polarity differences in the geometry and force_coordinate.

The returned transformation matrix is intended to be used as an output transformation matrix for MIMO vibration control.

write_to_unv¶

• Source: GitHub

def sdynpy.ShapeArray.write_to_unv(self, filename, specific_data_type=12, load_case_number=0)

Writes shape data to a unverisal file

Parameters¶

• filename : str Filename to which the geometry will be written. If None, unv data will be returned instead, similar to that obtained from the readunv function in sdynpy

• specific_data_type : int, optional Integer specifying the type of data in the shape. The default is 12, which denotes acceleration shapes.

• load_case_number : int, optional The load case number. The default is 0.

Returns¶

• shape_unv : List A list of Sdynpy_UFF_Dataset_55 objects, only if filename is None

Raises¶

• NotImplementedError

• Raised if complex numbers are used as they are not implemented yet