femio.util package

Submodules

femio.util.brick_generator module

femio.util.brick_generator.generate_brick(element_type, n_x_element, n_y_element, n_z_element=None, *, x_length=1.0, y_length=1.0, z_length=1.0)

Generate brick mesh.

Parameters:

  • element_type (str) – Element type selected from [‘tri’, ‘quad’, ‘tet’, ‘hex’].

  • n_x_element (int) – The number of element in the X direction.

  • n_y_element (int) – The number of element in the Y direction.

  • n_z_element (int, optional) – The number of element in the Z direction.

  • x_length (float, optional) – The X length of the brick. The default is 1.

  • y_length (float, optional) – The Y length of the brick. The default is 1.

  • z_length (float, optional) – The Z length of the brick. The default is 1.

Returns:

fem_data – FEMData object of the generated brick mesh.

Return type:

femio.FEMData

femio.util.random_generator module

femio.util.random_generator.generate_random_mesh(element_type, n_point, *, x_length=1.0, y_length=1.0, z_length=1.0, quality_threshold=None, noise_scale=1.0, strip_epsilon=None)

Generate random mesh.

Parameters:

  • element_type (str) – Element type selected from [‘tri’, ‘quad’, ‘tet’, ‘hex’].

  • n_point (int) – The number of points.

  • x_length (float, optional) – The maximum X length of the mesh. The default is 1.

  • y_length (float, optional) – The maximum Y length of the mesh. The default is 1.

  • z_length (float, optional) – The maximum Z length of the mesh. The default is 1.

  • quality_threshold (float, optional) – If fed, compute quality of elements and remove some when the quality is bad.

  • noise_scale (float, optional) – The scale of the noise added to node positions. The default is 0.1.

  • strip_epsilon (float, optional) – If True, remove superfacial elements, which tend to have bad quality.

Returns:

fem_data – FEMData object of the generated brick mesh.

Return type:

femio.FEMData

femio.util.string_parser module

class femio.util.string_parser.HeaderData(headers, list_indices, data)

Bases: object

extract_data(key, *, concatenate=True)
extract_headers(key)
class femio.util.string_parser.ListStringSeries(list_string_series)

Bases: object

expand_include(pattern, base_name)
strip()
class femio.util.string_parser.StringSeries(*args, **kw)

Bases: Series

classmethod concat(list_data, axis=0)
connect(other, delimiter=',')

Connect two StringSeries objects with specified delimiter. Lengths of two objects should be the same.

Parameters:

  • other – Other StringSeries object to be connected.

  • delimiter – String to appear at the connection.

Returns:

StringSeries object after connection.

classmethod connect_all(list_data, delimiter=',', str_format=None)
delimit(delimiter=',')

Delimit StringLines object with the specified delimiter to output rank-2 ndarray of strings.

Parameters:

delimiter – String of delimiter (default: ‘,’).

Returns:

rank-2 ndarray of string.

expand_include(pattern, base_name)

Expand data like ‘include’ statement. Expanded data is concatenated at the end of the non-expanded data.

Parameters:

  • pattern – Pattern showing include statement. Include file should be captured with the first expression.

  • base_name – Directory name of the include file location.

Returns:

StringSeries object after expansion.

extract_captures(pattern, *, convert_values=False)
find_match(pattern, *, allow_multiple_matches=True, convert_values=False, negative_match=False)

Find match to the specified pattern.

Parameters:

  • pattern – Pattern to be used for matching.

  • allow_multiple_matches – True to accept several matches. (Default = True)

  • convert_values – Bool, [True] Flag to convert StringSeries to values

Returns:

StringSeries or ndarray of matches.

indices_match_clusters(pattern, *, negative_match=False)

Make cluster of indices of matches. Cluster means a group with continuous indices.

Parameters:

pattern – Pattern to be used for matching.

Returns:

list of ndarrays containing indices of each cluster.

indices_matches(pattern, *, negative_match=False)

Return indices of matched lines.

Parameters:

pattern – Pattern to be used for matching.

Returns:

Ndarray of ints indicating indices of matched lines.

classmethod read_array(_array, *, delimiter=',', str_format=None)

Read array to make StringSeries object.

Parameters:

  • array – Ndarray or list of NDarray to make StringSeries object.

  • delimiter – String indicating delimiter to connect components in a raw (default: ‘,’).

  • str_format – Format string to be passed to numpy.savetxt.

Returns: StringSeries object after reading arrays.

classmethod read_file(file_name, *, pattern_ignore=None)

Read file and convert to numpy string array.

Parameters:

  • file_name – String of file name.

  • pattern_ignore – String to be used for ignore unecessary line e.g. comment.

Returns:

StringDataFrame object. Each component corresponds to each line of the input file.

classmethod read_files(file_names, *, pattern_ignore=None, separate=False)

Read files.

Parameters:

  • file_names – Array of strings indicating file names.

  • pattern_ignore – String to be used for ignore unecessary line e.g. comment.

  • separate – bool If True, return separated contents, namely, ListStringSeries object.

Returns:

StringDataFrame object. Each component corresponds to each line of input files (contents are concatenated).

split_vertical(index_cut, delimiter=',')

Split StringSeries object vertically.

Parameters:

index_cut – Index (= start index of 2nd obj) to cut the StringLines.

Returns:

2-tuple of DataFrame objects after splitting.

split_vertical_all(delimiter=',')

Split StringSeries object vertically. Output will be n StringSeries objects.

Args: :returns: n-tuple of StringSeries objexts after splitting.

strip()
to_dict_fem_attributes(names, component_nums, data_units=None, delimiter=',')

Generate dict of FEMAttribute objects with parsing the lines.

Parameters:

  • names – List of strings indicating names of the attributes.

  • component_nums – List of ints indicating # of components of each attributes.

  • data_units – List of strings indicating unit of data. (default: ‘unit_unknown’)

Returns:

Dict with key = name, value = fem.FEMAttribute.

to_fem_attribute(name, id_column, slice_data_columns, *, data_type=<class 'float'>, delimiter=', ', data_unit='unit_unknown', generate_id2index=False)

Generate FEMAttribute object with parsing the series.

Parameters:

  • name – String indicating name of the attribute.

  • lines – Ndarray of strings contains data.

  • id_column – Int indicating the column of ids.

  • slice_data_columns – Slice object indicating the columns of data.

  • data_type – Type of the data (default: float)

  • delimiter – String of delimiter. (default: ‘,’)

  • data_unit – String indicating unit of data. (default: ‘unit_unknown’)

  • generate_id2index – bool If True, generate pandas.DataFrame of IDs and indices.

Returns:

femio.FEMAttribute object.

to_header_data(pattern)
to_values(delimiter=', ', data_type=<class 'float'>, to_rank1=False, until_column=None)

Delimit StringLines object with the specified delimiter to output ndarray of the specified data_type.

Parameters:

  • delimiter – String of delimiter (default: ‘,’).

  • data_type – Type of output data (default: float).

  • to_rank1 – Boolean to control output (True: rank-1, False: rank-2, default: False)

  • until_column – int, optional, [None] Read until the specified column.

Returns:

Ndarray of the specified data_type.

Module contents