Utils.writer(文件保存函数) 模块

ppsci.utils.writer

save_csv_file(filename, data_dict, keys, alias_dict=None, use_header=True, delimiter=',', encoding='utf-8')

Write numpy or tensor data into csv file.

Parameters:

Name	Type	Description	Default
filename	str	Dump file path.	required
data_dict	Dict[str, Union[ndarray, Tensor]]	Numpy or tensor data in dict.	required
keys	Tuple[str, ...]	Keys for data_dict to be fetched.	required
alias_dict	Optional[Dict[str, str]]	Alias dict for keys, i.e. {dump_key: dict_key}. Defaults to None.	None
use_header	bool	Whether save csv with header. Defaults to True.	True
delimiter	str	Delemiter for splitting different data field. Defaults to ",".	','
encoding	str	Encoding. Defaults to "utf-8".	'utf-8'

Examples:

>>> import numpy as np
>>> from ppsci.utils import save_csv_file
>>> data_dict = {
...     "a": np.array([[1], [2], [3]]).astype("int64"), # [3, 1]
...     "b": np.array([[4.12], [5.25], [6.3370]]).astype("float32"), # [3, 1]
... }
>>> save_csv_file(
...     "test.csv",
...     data_dict,
...     ("A", "B"),
...     alias_dict={"A": "a", "B": "b"},
...     use_header=True,
...     delimiter=",",
...     encoding="utf-8",
... )

>>> # == test.csv ==
>>> # A,B
>>> # 1,4.12
>>> # 2,5.25
>>> # 3,6.337

Source code in ppsci/utils/writer.py

def save_csv_file(
    filename: str,
    data_dict: Dict[str, Union[np.ndarray, "paddle.Tensor"]],
    keys: Tuple[str, ...],
    alias_dict: Optional[Dict[str, str]] = None,
    use_header: bool = True,
    delimiter: str = ",",
    encoding: str = "utf-8",
):
    """Write numpy or tensor data into csv file.

    Args:
        filename (str): Dump file path.
        data_dict (Dict[str, Union[np.ndarray, paddle.Tensor]]): Numpy or tensor data in dict.
        keys (Tuple[str, ...]): Keys for data_dict to be fetched.
        alias_dict (Optional[Dict[str, str]], optional): Alias dict for keys,
            i.e. {dump_key: dict_key}. Defaults to None.
        use_header (bool, optional): Whether save csv with header. Defaults to True.
        delimiter (str, optional): Delemiter for splitting different data field. Defaults to ",".
        encoding (str, optional): Encoding. Defaults to "utf-8".

    Examples:
        >>> import numpy as np
        >>> from ppsci.utils import save_csv_file
        >>> data_dict = {
        ...     "a": np.array([[1], [2], [3]]).astype("int64"), # [3, 1]
        ...     "b": np.array([[4.12], [5.25], [6.3370]]).astype("float32"), # [3, 1]
        ... }
        >>> save_csv_file(
        ...     "test.csv",
        ...     data_dict,
        ...     ("A", "B"),
        ...     alias_dict={"A": "a", "B": "b"},
        ...     use_header=True,
        ...     delimiter=",",
        ...     encoding="utf-8",
        ... )  # doctest: +SKIP

        >>> # == test.csv ==
        >>> # A,B
        >>> # 1,4.12
        >>> # 2,5.25
        >>> # 3,6.337
    """
    if alias_dict is None:
        alias_dict = {}

    # convert to numpy array
    data_fields = []
    header = []
    for key in keys:
        fetch_key = alias_dict.get(key, key)
        data = data_dict[fetch_key]
        if isinstance(data, paddle.Tensor):
            data = data.numpy()  # [num_of_samples, ]

        if isinstance(data, np.ndarray):
            data = data.flatten()
        data_fields.append(data)

        header.append(key)

    assert len(header) == len(data_fields)

    data_fields = zip(*data_fields)  # transpose col data to row data
    with open(filename, "w", newline="", encoding=encoding) as file:
        writer = csv.writer(file, delimiter=delimiter)

        if use_header:
            writer.writerow(header)

        writer.writerows(data_fields)

    logger.message(f"csv file has been dumped to: {filename}")

save_tecplot_file(filename, data_dict, keys, num_x, num_y, alias_dict=None, delimiter=' ', encoding='utf-8', num_timestamps=1)

Write numpy or tensor data into tecplot file(s).

Parameters:

Name	Type	Description	Default
filename	str	Tecplot file path.	required
data_dict	Dict[str, Union[ndarray, Tensor]]	Numpy or Tensor data in dict.	required
keys	Tuple[str, ...]	Target keys to be dumped.	required
num_x	int	The number of discrete points of the grid in the X-axis. Assuming the discrete grid size is 20 x 30, then num_x=20.	required
num_y	int	The number of discrete points of the grid in the Y-axis. Assuming the discrete grid size is 20 x 30, then num_y=30.	required
alias_dict	Optional[Dict[str, str]]	Alias dict for keys, i.e. {dump_key: dict_key}. Defaults to None.	None
delimiter	str	Delemiter for splitting different data field. Defaults to " ".	' '
encoding	str	Encoding. Defaults to "utf-8".	'utf-8'
num_timestamps	int	Number of timestamp over coord and value. Defaults to 1.	1

Examples:

>>> import numpy as np
>>> from ppsci.utils import save_tecplot_file
>>> data_dict = {
...     "x": np.array([[-1.0], [-1.0], [-1.0], [-1.0], [-1.0], [-1.0]]), # [6, 1]
...     "y": np.array([[1.0], [2.0], [3.0], [1.0], [2.0], [3.0]]), # [6, 1]
...     "value": np.array([[3], [33], [333], [3333], [33333], [333333]]), # [6, 1]
... }
>>> save_tecplot_file(
...    "./test.dat",
...    data_dict,
...    ("X", "Y", "value"),
...    num_x=1,
...    num_y=3,
...    alias_dict={"X": "x", "Y": "y"},
...    num_timestamps=2,
... )
>>> # == test_t-0.dat ==
>>> # title = "./test_t-0.dat"
>>> # variables = "X", "Y"
>>> # Zone I = 3, J = 1, F = POINT
>>> # -1.0 1.0 3.0
>>> # -1.0 2.0 33.0
>>> # -1.0 3.0 333.0

>>> # == test_t-1.dat ==
>>> # title = "./test_t-1.dat"
>>> # variables = "X", "Y"
>>> # Zone I = 3, J = 1, F = POINT
>>> # -1.0 1.0 3333.0
>>> # -1.0 2.0 33333.0
>>> # -1.0 3.0 333333.0

Source code in ppsci/utils/writer.py

def save_tecplot_file(
    filename: str,
    data_dict: Dict[str, Union[np.ndarray, "paddle.Tensor"]],
    keys: Tuple[str, ...],
    num_x: int,
    num_y: int,
    alias_dict: Optional[Dict[str, str]] = None,
    delimiter: str = " ",
    encoding: str = "utf-8",
    num_timestamps: int = 1,
):
    """Write numpy or tensor data into tecplot file(s).

    Args:
        filename (str): Tecplot file path.
        data_dict (Dict[str, Union[np.ndarray, paddle.Tensor]]): Numpy or Tensor data in dict.
        keys (Tuple[str, ...]): Target keys to be dumped.
        num_x (int): The number of discrete points of the grid in the X-axis. Assuming
            the discrete grid size is 20 x 30, then num_x=20.
        num_y (int): The number of discrete points of the grid in the Y-axis. Assuming
            the discrete grid size is 20 x 30, then num_y=30.
        alias_dict (Optional[Dict[str, str]], optional): Alias dict for keys,
            i.e. {dump_key: dict_key}. Defaults to None.
        delimiter (str, optional): Delemiter for splitting different data field. Defaults to " ".
        encoding (str, optional): Encoding. Defaults to "utf-8".
        num_timestamps (int, optional): Number of timestamp over coord and value. Defaults to 1.

    Examples:
        >>> import numpy as np
        >>> from ppsci.utils import save_tecplot_file
        >>> data_dict = {
        ...     "x": np.array([[-1.0], [-1.0], [-1.0], [-1.0], [-1.0], [-1.0]]), # [6, 1]
        ...     "y": np.array([[1.0], [2.0], [3.0], [1.0], [2.0], [3.0]]), # [6, 1]
        ...     "value": np.array([[3], [33], [333], [3333], [33333], [333333]]), # [6, 1]
        ... }
        >>> save_tecplot_file(
        ...    "./test.dat",
        ...    data_dict,
        ...    ("X", "Y", "value"),
        ...    num_x=1,
        ...    num_y=3,
        ...    alias_dict={"X": "x", "Y": "y"},
        ...    num_timestamps=2,
        ... )  # doctest: +SKIP
        >>> # == test_t-0.dat ==
        >>> # title = "./test_t-0.dat"
        >>> # variables = "X", "Y"
        >>> # Zone I = 3, J = 1, F = POINT
        >>> # -1.0 1.0 3.0
        >>> # -1.0 2.0 33.0
        >>> # -1.0 3.0 333.0


        >>> # == test_t-1.dat ==
        >>> # title = "./test_t-1.dat"
        >>> # variables = "X", "Y"
        >>> # Zone I = 3, J = 1, F = POINT
        >>> # -1.0 1.0 3333.0
        >>> # -1.0 2.0 33333.0
        >>> # -1.0 3.0 333333.0
    """
    if alias_dict is None:
        alias_dict = {}

    ntxy = len(next(iter(data_dict.values())))
    if ntxy % num_timestamps != 0:
        raise ValueError(
            f"num_points({ntxy}) must be a multiple of "
            f"num_timestamps({num_timestamps})."
        )
    nxy = ntxy // num_timestamps

    nx, ny = num_x, num_y
    assert nx * ny == nxy, f"nx({nx}) * ny({ny}) != nxy({nxy})"

    if len(os.path.dirname(filename)):
        os.makedirs(os.path.dirname(filename), exist_ok=True)

    if filename.endswith(".dat"):
        filename = filename[:-4]

    for t in range(num_timestamps):
        # write 1 tecplot file for each timestep
        if num_timestamps > 1:
            dump_filename = f"{filename}_t-{t}.dat"
        else:
            dump_filename = f"{filename}.dat"

        fetch_keys = [alias_dict.get(key, key) for key in keys]
        with open(dump_filename, "w", encoding=encoding) as f:
            # write meta information of tec
            f.write(f'title = "{dump_filename}"\n')
            header = ", ".join([f'"{key}"' for key in keys])
            f.write(f"variables = {header}\n")

            # NOTE: Tecplot is column-major, so we need to specify I = ny, J = nx,
            # which is in contrast to our habits.
            f.write(f"Zone I = {ny}, J = {nx}, F = POINT\n")

            # write points data into file
            data_cur_time_step = [
                data_dict[key][t * nxy : (t + 1) * nxy] for key in fetch_keys
            ]

            for items in zip(*data_cur_time_step):
                f.write(delimiter.join([str(float(x)) for x in items]) + "\n")

    if num_timestamps > 1:
        logger.message(
            f"tecplot files are saved to: {filename}_t-0.dat ~ {filename}_t-{num_timestamps - 1}.dat"
        )
    else:
        logger.message(f"tecplot file is saved to: {filename}.dat")