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export.py

ExportMixin

Mixin class providing data/metadata export tools for by level-set solver.

Attributes

T_slices: dict Dictionary of time T slices of evolving surface and its properties.

Methods:

export_data 

export_data(dir: str, filename: str = 'T_slices') -> None

Tool to export time T slice data to HDF5 file.

Parameters

dir: str Path to directory to write HDF5 file into. filename: str="T_slices" Name of HDF file.

Source code in erosionfront/levelset/export.py 
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def export_data(
        self,
        dir: str, 
        filename: str="T_slices",
    ) -> None:
    """
    Tool to export time T slice data to HDF5 file.

    Parameters
    ----------
    dir: str
        Path to directory to write HDF5 file into.
    filename: str="T_slices"
        Name of HDF file.
    """
    file_path: str = os.path.join(dir, f"{filename}.hdf5",)
    file_: hd5File
    with h5py.File(file_path, "w",) as file_:
        i_: int
        for i_ in self.T_slices:
            T_group_ = file_.create_group(f"{self.T_slices[i_]['T']}")
            T_group_.create_dataset("x", data=self.T_slices[i_]["x"])
            T_group_.create_dataset("z", data=self.T_slices[i_]["z"])
            T_group_.create_dataset("β", data=self.T_slices[i_]["β"])

export_metadata 

export_metadata(dir: str, filename: str = 'metadata') -> dict

Tool to simulation metadata to JSON file.

Parameters

dir: str Path to directory to write JSON file into. filename: str="metadata" Name of JSON file.

Source code in erosionfront/levelset/export.py 
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def export_metadata(
        self, 
        dir: str, 
        filename: str="metadata",
    ) -> dict:
    """
    Tool to simulation metadata to JSON file.

    Parameters
    ----------
    dir: str
        Path to directory to write JSON file into.
    filename: str="metadata"
        Name of JSON file.
    """
    file_path: str = os.path.join(dir, f"{filename}.json",)
    metadata: dict = {}
    for group_ in (".", "domain", "surface", "substrate", "model", "gma",):
        if group_==".":
            object_ = self
            group_ = "sim"
        else:
            object_ = getattr(self, group_)
        metadata[group_] = {}
        for attr_ in object_.__dict__:
            value_ = object_.__dict__[attr_]
            type_ = type(value_)
            if is_serializable(value_):
                if type_ is tuple and not isinstance(value_[0], np.ndarray):
                    metadata[group_].update({
                        attr_: [
                            make_serializable(element_)
                            for element_ in value_
                        ]
                    })
                else:
                    metadata[group_].update({
                        attr_: make_serializable(value_)
                    })
            else:
                if type_ is np.ndarray and value_.size<100:
                    metadata[group_].update({
                        attr_: make_serializable(value_)
                    })
    file_: TextIOWrapper
    with open(file_path, "w",) as file_:
        json.dump(
            metadata, 
            file_, 
            indent=4,
            ensure_ascii=False,
        )
    return metadata