ansys.dyna.core.lib.deck_plotter#
Functions#
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Given a node id, output the node index as a dict |
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Merge mesh keywords. |
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Get the shell facet array from the DataFrame. |
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Get the solid array from the DataFrame. |
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Convert DataFrame to lines array. |
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Convert mapping to numpy array. |
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Extract shell faces from DataFrame. |
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Extract lines from DataFrame. |
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Create the PolyData Object for plotting from a given deck with nodes and elements. |
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Plot the deck. |
Module Contents#
- ansys.dyna.core.lib.deck_plotter.get_nid_to_index_mapping(nodes) Dict[source]#
Given a node id, output the node index as a dict
- ansys.dyna.core.lib.deck_plotter.merge_keywords(deck: ansys.dyna.core.Deck) Tuple[pandas.DataFrame, Dict][source]#
Merge mesh keywords.
Given a deck, merges specific keywords (NODE, ELEMENT_SHELL, ELEMENT_BEAM, ELEMENT_SOLID) and returns tham as data frames.
- ansys.dyna.core.lib.deck_plotter.shell_facet_array(facets: pandas.DataFrame) numpy.array[source]#
Get the shell facet array from the DataFrame.
Facets are a pandas frame that is a sequence of integers or NAs with max length of 8. valid rows contain 3,4,6, or 8 items consecutive from the left. we don’t plot quadratic edges so 6/8 collapse to 3/4 invalid rows are ignored, meaning they return an empty array return an array of length 4 or 5 using the pyvista spec for facets which includes a length prefix [1,2,3]=>[3,1,2,3] [1,2,3,0]=>[3,1,2,3] [1,2,3,NA]=>[3,1,2,3]
- ansys.dyna.core.lib.deck_plotter.solid_array(solids: pandas.DataFrame)[source]#
Get the solid array from the DataFrame.
Solids are a pandas frame that is a sequence of integers or NAs with max length of 28. valid rows contain 3, 4, 6, or 8 items consecutive from the left. We don’t plot quadratic edges so 6/8 collapse to 3/4 invalid rows are ignored, meaning they return an empty array return an array of length 4 or 5 using the pyvista spec for facets which includes a length prefix [1,2,3]=>[3,1,2,3] [1,2,3,0]=>[3,1,2,3] [1,2,3,NA]=>[3,1,2,3]
- ansys.dyna.core.lib.deck_plotter.line_array(lines: pandas.DataFrame) numpy.array[source]#
Convert DataFrame to lines array.
lines is a pandas frame that is a sequence of integers or NAs with max length of 2. valid rows contain 2 items consecutive from the left. invalid rows are ignored, meaning they return an empty array return an array of length 3 using the pyvista spec for facets which includes a length prefix [1,2,]=>[2,1,2] [1,2,3,0]=>[] [1,2,3,NA]=>[]
- ansys.dyna.core.lib.deck_plotter.map_facet_nid_to_index(flat_facets: numpy.array, mapping: Dict) numpy.array[source]#
Convert mapping to numpy array.
Given a flat list of facets or lines, use the mapping from nid to python index to output the numbering system for pyvista from the numbering from dyna
- ansys.dyna.core.lib.deck_plotter.extract_shell_facets(shells: pandas.DataFrame, mapping)[source]#
Extract shell faces from DataFrame.
Shells table comes in with the form | eid | nid1 | nid2 | nid3 | nid4 | 1 | 10 | 11 | 12 | | 20 | 21 | 22 | 23 | 24
but the array needed for pyvista polydata is of the form where each element is prefixed by the length of the element node list [3,10,11,12,4,21,22,23,24]
Take individual rows, extract the appropriate nid’s and output a flat list of facets for pyvista
- ansys.dyna.core.lib.deck_plotter.extract_lines(beams: pandas.DataFrame, mapping: Dict[int, int]) numpy.ndarray[source]#
Extract lines from DataFrame.
Beams table comes in with the form with extra information not supported, | eid | nid1 | nid2 | 1 | 10 | 11 | 20 | 21 | 22
we only care about nid 1 and 2
but the array needed for pyvista polydata is the same as in extract facets of the form where each element is prefixed by the length of the element node list [2,10,11,2,21,22]
Take individual rows, extract the appropriate nid’s and output a flat list of facets for pyvista
- ansys.dyna.core.lib.deck_plotter.extract_solids(solids: pandas.DataFrame, mapping: Dict[int, int])[source]#