roxieapi.output.parser module

class roxieapi.output.parser.CoilGeomDfs(conductors: pandas.core.frame.DataFrame, strands: pandas.core.frame.DataFrame)

Bases: object

conductors: DataFrame

strands: DataFrame

class roxieapi.output.parser.EddyCurrentsData(xroot)

Bases: object

Data for eddy currents calculations for every time_step

get_eddy_currents_all_timesteps() → DataFrame

Retrieve eddy currents data for all time steps and store it in a pandas DataFrame.

Returns:

A DataFrame containing eddy currents data for all time steps. The columns are: - time_step: index of the time step - node: node number - elem: element number - jx: x component of the current density - jy: y component of the current density - jz: z component of the current density - j2/sigma: squared current density divided by the conductivity If no data exists, returns an empty DataFrame with the above columns.

Return type:

pd.DataFrame

get_eddy_currents_on_timestep(time_step_index=0) → DataFrame

Parse the eddy currents data from a specific time_step in the post.xml file.

Parameters:

time_step_index (int, optional) – The index of the <time_step> element from which to retrieve the <eddy_currents_data>. Defaults to 0, which is the first time_step.

Returns:

A DataFrame with the eddy currents data. The columns are - node: node number - elem: element number - jx: x component of the current density - jy: y component of the current density - jz: z component of the current density - j2/sigma: squared current density divided by the conductivity

Return type:

pd.DataFrame

get_elements() → DataFrame

Parse the <elements> data to extract details for each element.

Returns:

A DataFrame with columns: - ele_num: Element number - hmo_ele: HMO element - ele_type: Element type - ele_collector: Element collector - nodes: List of node numbers (up to max_nodes)

Return type:

pd.DataFrame

get_elements_info() → dict

Parse the <element_data> line to extract tot_ele and max_nodes.

Returns:

A dictionary containing: - “tot_ele” (int): The total number of elements. - “max_nodes” (int): The maximum number of nodes.

Return type:

dict

get_info_on_timestep(time_step_index: int) → dict

Parse the information for the specified <time_step> to extract step_number and absolute_time.

Parameters:

time_step_index (int) – The index of the <time_step> element to parse.

Returns:

A dictionary containing: - “step_number” (int): The time step number. - “absolute_time” (float): The absolute time for the present time step.

Return type:

dict

get_iron_nodal_data_info() → dict

Parse the <iron_nodal_data> line to extract tot_nodes, nodf, and dim.

Returns:

A dictionary containing: - “tot_nodes” (int): Total number of nodes. - “nodf” (int): Number of degrees of freedom. - “dim” (int): Dimensionality of the nodes.

Return type:

dict

get_magnetic_field_all_timesteps() → DataFrame

Retrieve magnetic field data for all time steps and store it in a pandas DataFrame.

Returns:

A DataFrame containing magnetic field data for all time steps. The columns are: - time_step: index of the time step - node: node number - elem: element number - Hx: x component of the magnetic field - Hy: y component of the magnetic field - Hz: z component of the magnetic field If no data exists, returns an empty DataFrame with the above columns.

Return type:

pd.DataFrame

get_magnetic_field_on_timestep(time_step_index: int) → DataFrame

Parse the magnetic field data from a specific time_step in the post.xml file.

Parameters:

time_step_index (int) – The index of the <time_step> element from which to retrieve the <magnetic_field_data>.

Returns:

A DataFrame with the magnetic field data. The columns are - node: node number - elem: element number - Hx: x component of the magnetic field - Hy: y component of the magnetic field - Hz: z component of the magnetic field

Return type:

pd.DataFrame

get_magnetic_induction_all_timesteps() → DataFrame

Retrieve magnetic induction data for all time steps and store it in a pandas DataFrame.

Returns:

A DataFrame containing magnetic induction data for all time steps. The columns are: - time_step: index of the time step - node: node number - elem: element number - Bx: x component of the magnetic induction - By: y component of the magnetic induction - Bz: z component of the magnetic induction If no data exists, returns an empty DataFrame with the above columns.

Return type:

pd.DataFrame

get_magnetic_induction_on_timestep(time_step_index: int) → DataFrame

Parse the magnetic induction data from a specific time_step in the post.xml file.

Parameters:

time_step_index (int) – The index of the <time_step> element from which to retrieve the <magnetic_induction_data>.

Returns:

A DataFrame with the magnetic induction data. The columns are - node: node number - elem: element number - Bx: x component of the magnetic induction - By: y component of the magnetic induction - Bz: z component of the magnetic induction

Return type:

pd.DataFrame

get_mesh_elements() → DataFrame | None

Extract mesh elements from the XML root element and return them as a pandas DataFrame.

Returns:

A DataFrame with mesh elements. Returns None if the “elements” tag is not found.

Return type:

Optional[pd.DataFrame]

get_nodal_coords() → ndarray | None

Extract node data from the XML root element and return it as a NumPy array.

Returns:

A NumPy array of node data. Returns None if the “nodes” tag is not found.

Return type:

Optional[np.ndarray]

get_potential_all_timesteps() → DataFrame

Retrieve potential data for all time steps and store it in a pandas DataFrame.

Returns:

A DataFrame containing potential data for all time steps. The columns are: - time_step: index of the time step - node: node number - az: potential value - norm_deriv_az: normalized derivative of az If no data exists, returns an empty DataFrame with the above columns.

Return type:

pd.DataFrame

get_potential_on_timestep(time_step_index: int) → DataFrame

Parse the potential data from a specific time_step in the post.xml file.

Parameters:

time_step_index (int) – The index of the <time_step> element from which to retrieve the <potential_data>.

Returns:

A DataFrame with the potential data. The columns are - node: node number - az: potential value - norm_deriv_az: normalized derivative of az

Return type:

pd.DataFrame

get_time_step_count() → int

Get the number of <time_step> elements in the XML file.

Returns:

The number of <time_step> elements found.

Return type:

int

parse_nodal_coord_info() → DataFrame

Parse the <nodal_coord> data to extract details for each node.

Returns:

A DataFrame with columns: - node_num: Node number - hmo_node: HMO node - x: X coordinate - y: Y coordinate - z: Z coordinate - frame: Frame value - bound: Boundary condition - nodf: Degrees of freedom

Return type:

pd.DataFrame

class roxieapi.output.parser.MeshGeomDfs(nodes: pandas.core.frame.DataFrame, elements: pandas.core.frame.DataFrame, boundaries: pandas.core.frame.DataFrame)

Bases: object

boundaries: DataFrame

elements: DataFrame

nodes: DataFrame

class roxieapi.output.parser.OptData(id: int, name: str)

Bases: object

Data Of an optimization Step

property blockGeometries3D: Dict[int, BlockGeometry]

property blockTopologies: Dict[int, BlockTopology]

Get block topologies

property brickGeometries3D: Dict[int, Brick3DGeometry]

property coilGeometries: Dict[int, CoilGeometry]

property coilGeometries3D: Dict[int, Coil3DGeometry]

property meshGeometries: Geometry | None

property meshGeometries3D: Geometry | None

property wedgeGeometries3D: Dict[int, WedgeGeometry]

class roxieapi.output.parser.RoxieOutputParser(xml_file: str)

Bases: object

Roxie output parser class.

Takes all different Roxie outputs, parses them, and provides a structured output of the results.

find_optstep(opt_step) → OptData | None

Find the optimization step data for a given optimization step

Parameters:

opt_step – The optimization step number

Returns:

The OptData object or None if not found

find_transstep(opt_step: int, trans_step: int) → TransStepData | None

Find the transient step data for a given optimization step and transient step

Parameters:

• opt_step – The optimization step number

• trans_step – The transient step number

Returns:

The TransStepData object or None if not found

get_3d_plot(plot_nr: int = 1) → Plot3D | None

Return the 3D plot with number i :param plon_nr: The plot number, defaults to 1 :return: The Plot3D definition, or None

get_conductor_forces(opt_step: int = 1, trans_step: int = 1) → DataFrame | None

Return Conductor forces for given Step, or None if not present

Parameters:

• opt_step – The Optimization step, defaults to 1

• trans_step – Transient step, defaults to 1

Returns:

The Conductor forces as Dataframe

get_crosssection_plot(plot_nr: int = 1) → Plot2D | None

Return the Crossection 2D plot with number i

Parameters:

plot_nr – The plot_number, defaults to 1

Returns:

The Plot2D object, or None

get_harmonic_coil(coil_nr: int = 1, opt_step: int = 1, trans_step: int = 1) → HarmonicCoil | None

Return the harmonic coil for given step and coil id, or None if not present

Parameters:

• coil_nr – Harmonic Coil Nr, defaults to 1

• opt_step – The Optimization Step Nr, defaults to 1

• trans_step – The Transient Step Nr, defaults to 1

Returns:

The Harmonic coil, or None

class roxieapi.output.parser.TransStepData(id: int, name: str)

Bases: object

Data of a transient step