"""This file contains functions for converting and storing jupyter notebooks."""
import nbformat
import pickle
import numpy as np
import os
from nbconvert import PythonExporter
from pathlib import Path  # for windows-Unix compatibility


def nbconvert_python(path):
    """Use nbconvert to convert jupyter notebook to python code.

    Return the string of python code. You can then excute it with `exec()`.

    Args:
        path (str): Path of jupyter notebook

    Returns:
        str: The string of python code converted from notebook

    """
    with open(path) as f:
        nb = nbformat.read(f, as_version=4)
    body, _ = PythonExporter().from_notebook_node(nb)
    return body


def is_picklable(obj):
    """Check if an obj can be dumped into a pickle file.

    Args:
        obj : The Object to be judged

    Returns:
        bool: The result if the input can be picklable

    """
    try:
        pickle.dumps(obj)
    except Exception:
        return False
    return True


def filter_pickable(global_vars):
    """Filter the variables that are pickable.

    Args:
        global_vars (array-like): The names of variables to get

    Returns:
        dict: Dictionary containing names of objects and their values

    """
    bk = {}
    for k in global_vars:
        obj = global_vars[k]
        if is_picklable(obj):
            try:
                bk.update({k: obj})
            except TypeError:
                pass
    return bk


def notebook_to_pickable_dict(path):
    """Excute jupyter notebook and then save variables defined in notebook.

    This function converts notebook to python code and then excutes the code.
    Finally it put all public variables that defined in notebook into dictionary
    and return it.
    Parameters
    ----------
    path : str
        Path of jupyter notebook
    Returns
    -------
    bk : :dict
        Dictionary containing names of variables and variables that defined in notebook.
    """
    # Step 1: Convert notebook to script
    code = nbconvert_python(path)
    code = code.replace("get_ipython()", "# get_ipython()")

    # Step 2: Execute script and save variables in dictionary
    d = {}
    exec(code, d)
    d.pop("__builtins__")

    # Step 3: Filter for pickable variables
    bk = filter_pickable(d)
    return bk


def save_to_pkl(path, obj):
    """Save object to pickle file.

    Args:
        path (str): Path to save pickle file
        obj : Object to be saved

    """
    with open(path, "wb") as f:
        pickle.dump(obj, f)


def basic_type_or_list(obj):
    """Check type of object."""
    return not np.asanyarray(obj).dtype.hasobject


def flatten_to_dict(obj):
    """Reduce dimensionality of dictionary."""

    def _flatten(value, key):
        """Reduce dimensionality of object recursively."""
        if isinstance(value, (list, tuple, set)):
            if basic_type_or_list(value):
                return {key: value} if key is not None else value
            else:
                tile_d = {}
                for i, v in enumerate(value):
                    tile_d.update(_flatten(v, f"{key}_{i}" if key is not None else i))
                return tile_d
        elif isinstance(value, dict):
            tile_d = {}
            for k, v in value.items():
                tile_d.update(_flatten(v, f"{key}_{k}" if key is not None else k))
            return tile_d
        else:
            return {key: value} if key is not None else value

    return _flatten(value=obj, key=None)


def to_ndarray(obj):
    """Convert to numpy array."""
    if isinstance(obj, dict):
        return {k: np.asanyarray(v) for k, v in obj.items()}
    elif isinstance(obj, (list, tuple, set)) and not basic_type_or_list(obj):
        return [np.asanyarray(v) for v in obj]
    else:
        return np.asanyarray(obj)


def is_path(path):
    """Judge if object is path or string of exists path."""
    if isinstance(path, os.PathLike):
        return True
    if not isinstance(path, str):
        return False
    return os.path.exists(path)


def contains_path(obj):
    """Judge if an array contains path."""
    if isinstance(obj, (np.ndarray, list, tuple, set)):
        for v in obj:
            if is_path(v):
                return True
        return False
    else:
        return is_path(obj)


def notebook_exec_result_flattened(path):
    """Prepare notebook for numpy savez."""
    # Step 1: Convert notebook to script
    code = nbconvert_python(path)
    code = code.replace("get_ipython()", "# get_ipython()")

    # Step 2: Execute script and save variables in dictionary
    d = {}
    exec(code, d)
    d.pop("__builtins__")

    # Step 3: Flatten all variables
    bk = flatten_to_dict(d)

    # Step 4: Filter for variables which is basic type or list of basic type
    bk_filted = {k: v for k, v in bk.items() if basic_type_or_list(v)}

    # Step 5: Remove environmental variables
    bk_filted = {k: v for k, v in bk_filted.items() if not contains_path(v)}
    for key in {"__warningregistry___version"}:
        bk_filted.pop(key)

    return bk_filted


def main():
    """Excute jupyter notebook and save global variables."""
    notebook_path = Path("docs/getting_started.ipynb")

    bk = notebook_exec_result_flattened(notebook_path)

    # to save session
    save_path = Path("pydsge/tests/resources/getting_started_stable.npz")
    with open(save_path, "wb") as f:
        np.savez_compressed(f, **bk)


if __name__ == "__main__":
    main()

"""This file contains functions for converting and storing jupyter notebooks."""
import nbformat
import pickle
import numpy as np
import os
from nbconvert import PythonExporter
from pathlib import Path  # for windows-Unix compatibility


def nbconvert_python(path):
    """Use nbconvert to convert jupyter notebook to python code.

    Return the string of python code. You can then excute it with `exec()`.

    Args:
        path (str): Path of jupyter notebook

    Returns:
        str: The string of python code converted from notebook

    """
    with open(path) as f:
        nb = nbformat.read(f, as_version=4)
    body, _ = PythonExporter().from_notebook_node(nb)
    return body


def is_picklable(obj):
    """Check if an obj can be dumped into a pickle file.

    Args:
        obj : The Object to be judged

    Returns:
        bool: The result if the input can be picklable

    """
    try:
        pickle.dumps(obj)
    except Exception:
        return False
    return True


def filter_pickable(global_vars):
    """Filter the variables that are pickable.

    Args:
        global_vars (array-like): The names of variables to get

    Returns:
        dict: Dictionary containing names of objects and their values

    """
    bk = {}
    for k in global_vars:
        obj = global_vars[k]
        if is_picklable(obj):
            try:
                bk.update({k: obj})
            except TypeError:
                pass
    return bk


def notebook_to_pickable_dict(path):
    """Excute jupyter notebook and then save variables defined in notebook.

    This function converts notebook to python code and then excutes the code.
    Finally it put all public variables that defined in notebook into dictionary
    and return it.
    Parameters
    ----------
    path : str
        Path of jupyter notebook
    Returns
    -------
    bk : :dict
        Dictionary containing names of variables and variables that defined in notebook.
    """
    # Step 1: Convert notebook to script
    code = nbconvert_python(path)
    code = code.replace("get_ipython()", "# get_ipython()")

    # Step 2: Execute script and save variables in dictionary
    d = {}
    exec(code, d)
    d.pop("__builtins__")

    # Step 3: Filter for pickable variables
    bk = filter_pickable(d)
    return bk


def save_to_pkl(path, obj):
    """Save object to pickle file.

    Args:
        path (str): Path to save pickle file
        obj : Object to be saved

    """
    with open(path, "wb") as f:
        pickle.dump(obj, f)


def basic_type_or_list(obj):
    """Check type of object."""
    return not np.asanyarray(obj).dtype.hasobject


def flatten_to_dict(obj):
    """Reduce dimensionality of dictionary."""

    def _flatten(value, key):
        """Reduce dimensionality of object recursively."""
        if isinstance(value, (list, tuple, set)):
            if basic_type_or_list(value):
                return {key: value} if key is not None else value
            else:
                tile_d = {}
                for i, v in enumerate(value):
                    tile_d.update(_flatten(v, f"{key}_{i}" if key is not None else i))
                return tile_d
        elif isinstance(value, dict):
            tile_d = {}
            for k, v in value.items():
                tile_d.update(_flatten(v, f"{key}_{k}" if key is not None else k))
            return tile_d
        else:
            return {key: value} if key is not None else value

    return _flatten(value=obj, key=None)


def to_ndarray(obj):
    """Convert to numpy array."""
    if isinstance(obj, dict):
        return {k: np.asanyarray(v) for k, v in obj.items()}
    elif isinstance(obj, (list, tuple, set)) and not basic_type_or_list(obj):
        return [np.asanyarray(v) for v in obj]
    else:
        return np.asanyarray(obj)


def is_path(path):
    """Judge if object is path or string of exists path."""
    if isinstance(path, os.PathLike):
        return True
    if not isinstance(path, str):
        return False
    return os.path.exists(path)


def contains_path(obj):
    """Judge if an array contains path."""
    if isinstance(obj, (np.ndarray, list, tuple, set)):
        for v in obj:
            if is_path(v):
                return True
        return False
    else:
        return is_path(obj)


def notebook_exec_result_flattened(path):
    """Prepare notebook for numpy savez."""
    # Step 1: Convert notebook to script
    code = nbconvert_python(path)
    code = code.replace("get_ipython()", "# get_ipython()")

    # Step 2: Execute script and save variables in dictionary
    d = {}
    exec(code, d)
    d.pop("__builtins__")

    # Step 3: Flatten all variables
    bk = flatten_to_dict(d)

    # Step 4: Filter for variables which is basic type or list of basic type
    bk_filted = {k: v for k, v in bk.items() if basic_type_or_list(v)}

    # Step 5: Remove environmental variables
    bk_filted = {k: v for k, v in bk_filted.items() if not contains_path(v)}
    for key in {"__warningregistry___version"}:
        bk_filted.pop(key)

    return bk_filted


def main():
    """Excute jupyter notebook and save global variables."""
    notebook_path = Path("docs/getting_started.ipynb")

    bk = notebook_exec_result_flattened(notebook_path)

    # to save session
    save_path = Path("pydsge/tests/resources/getting_started_stable.npz")
    with open(save_path, "wb") as f:
        np.savez_compressed(f, **bk)


if __name__ == "__main__":
    main()

This file contains functions for converting and storing jupyter notebooks. # for windows-Unix compatibility Use nbconvert to convert jupyter notebook to python code.      Return the string of python code. You can then excute it with `exec()`.      Args:         path (str): Path of jupyter notebook      Returns:         str: The string of python code converted from notebook Check if an obj can be dumped into a pickle file.      Args:         obj : The Object to be judged      Returns:         bool: The result if the input can be picklable Filter the variables that are pickable.      Args:         global_vars (array-like): The names of variables to get      Returns:         dict: Dictionary containing names of objects and their values Excute jupyter notebook and then save variables defined in notebook.      This function converts notebook to python code and then excutes the code.     Finally it put all public variables that defined in notebook into dictionary     and return it.     Parameters     ----------     path : str         Path of jupyter notebook     Returns     -------     bk : :dict         Dictionary containing names of variables and variables that defined in notebook. # Step 1: Convert notebook to script # Step 2: Execute script and save variables in dictionary # Step 3: Filter for pickable variables Save object to pickle file.      Args:         path (str): Path to save pickle file         obj : Object to be saved Check type of object. Reduce dimensionality of dictionary. Reduce dimensionality of object recursively. Convert to numpy array. Judge if object is path or string of exists path. Judge if an array contains path. Prepare notebook for numpy savez. # Step 1: Convert notebook to script # Step 2: Execute script and save variables in dictionary # Step 3: Flatten all variables # Step 4: Filter for variables which is basic type or list of basic type # Step 5: Remove environmental variables Excute jupyter notebook and save global variables. # to save session