# coding: utf-8
# Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department
# Distributed under the terms of "New BSD License", see the LICENSE file.

from __future__ import print_function

import h5py
import os
import importlib
import pandas
import posixpath
import h5io
import numpy as np
from tables.exceptions import NoSuchNodeError, HDF5ExtError
import sys

"""
Classes to map the Python objects to HDF5 data structures
"""

__author__ = "Joerg Neugebauer, Jan Janssen"
__copyright__ = (
    "Copyright 2019, Max-Planck-Institut für Eisenforschung GmbH - "
    "Computational Materials Design (CM) Department"
)
__version__ = "1.0"
__maintainer__ = "Jan Janssen"
__email__ = "janssen@mpie.de"
__status__ = "production"
__date__ = "Sep 1, 2017"


class HDFStoreIO(pandas.HDFStore):
    """
    dict-like IO interface for storing pandas objects in PyTables either Fixed or Table format.
    - copied from pandas.HDFStore

    Args:
        path (str) : File path to HDF5 file
        mode (str): {'a', 'w', 'r', 'r+'}, default 'a'
                    ``'r'``
                        Read-only; no data can be modified.
                    ``'w'``
                        Write; a new file is created (an existing file with the same name would be deleted).
                    ``'a'``
                        Append; an existing file is opened for reading and writing, and if the file does not exist it
                        is created.
                    ``'r+'``
                        It is similar to ``'a'``, but the file must already exist.
        complevel (int): 1-9, default 0
                         If a complib is specified compression will be applied
                         where possible
        complib (str): {'zlib', 'bzip2', 'lzo', 'blosc', None}, default None
                       If complevel is > 0 apply compression to objects written in the store wherever possible
        fletcher32 (bool): bool, default False
                           If applying compression use the fletcher32 checksum
    """

    def __init__(
        self, path, mode=None, complevel=None, complib=None, fletcher32=False, **kwargs
    ):
        super(HDFStoreIO, self).__init__(
            path,
            mode=mode,
            complevel=complevel,
            complib=complib,
            fletcher32=fletcher32,
            **kwargs
        )

    def open(self, **kwargs):
        """
        Open the file in the specified mode - copied from pandas.HDFStore.open()

        Args:
            **kwargs: mode : {'a', 'w', 'r', 'r+'}, default 'a'
                      See HDFStore docstring or tables.open_file for info about modes
        Returns:
            HDFStoreIO: self - in contrast to the original implementation in pandas.
        """
        super(HDFStoreIO, self).open(**kwargs)
        return self


class FileHDFio(object):
    """
    Class that provides all info to access a h5 file. This class is based on h5io.py, which allows to
    get and put a large variety of jobs to/from h5

    Args:
        file_name (str): absolute path of the HDF5 file
        h5_path (str): absolute path inside the h5 path - starting from the root group
        mode (str): mode : {'a', 'w', 'r', 'r+'}, default 'a'
                    See HDFStore docstring or tables.open_file for info about modes

    .. attribute:: file_name
        absolute path to the HDF5 file
    .. attribute:: h5_path
        path inside the HDF5 file - also stored as absolute path
    .. attribute:: history
        previously opened groups / folders
    .. attribute:: file_exists
        boolean if the HDF5 was already written
    .. attribute:: base_name
        name of the HDF5 file but without any file extension
    .. attribute:: file_path
        directory where the HDF5 file is located
    .. attribute:: is_root
        boolean if the HDF5 object is located at the root level of the HDF5 file
    .. attribute:: is_open
        boolean if the HDF5 file is currently opened - if an active file handler exists
    .. attribute:: is_empty
        boolean if the HDF5 file is empty
    """

    def __init__(self, file_name, h5_path="/", mode="a"):
        if not os.path.isabs(file_name):
            raise ValueError("file_name must be given as absolute path name")
        self._file_name = None
        self.file_name = file_name
        self.history = []
        self.h5_path = h5_path
        self._filter = ["groups", "nodes", "objects"]

    @property
    def file_exists(self):
        """
        Check if the HDF5 file exists already

        Returns:
            bool: [True/False]
        """
        if os.path.isfile(self.file_name):
            return True
        else:
            return False

    @property
    def file_name(self):
        """
        Get the file name of the HDF5 file

        Returns:
            str: absolute path to the HDF5 file
        """
        return self._file_name

    @file_name.setter
    def file_name(self, new_file_name):
        """
        Set the file name of the HDF5 file

        Args:
            new_file_name (str): absolute path to the HDF5 file
        """
        self._file_name = os.path.abspath(new_file_name).replace("\\", "/")

    @property
    def base_name(self):
        """
        Name of the HDF5 file - but without the file extension .h5

        Returns:
            str: file name without the file extension
        """
        return ".".join(posixpath.basename(self.file_name).split(".")[:-1])

    @property
    def file_path(self):
        """
        Path where the HDF5 file is located - posixpath.dirname()

        Returns:
            str: HDF5 file location
        """
        return posixpath.dirname(self.file_name)

    @property
    def h5_path(self):
        """
        Get the path in the HDF5 file starting from the root group - meaning this path starts with '/'

        Returns:
            str: HDF5 path
        """
        return self._h5_path

    @h5_path.setter
    def h5_path(self, path):
        """
        Set the path in the HDF5 file starting from the root group

        Args:
            path (str): HDF5 path
        """
        if (path is None) or (path == ""):
            path = "/"
        self._h5_path = posixpath.normpath(path)
        if not posixpath.isabs(self._h5_path):
            self._h5_path = "/" + self._h5_path
        self._h5_group = "store.root" + self._h5_path.replace("/", ".")
        if self._h5_group[-1] != ".":
            self._h5_group += "."

    @property
    def is_root(self):
        """
        Check if the current h5_path is pointing to the HDF5 root group.

        Returns:
            bool: [True/False]
        """
        return "/" == self.h5_path

    # @property
    # def is_open(self):
    #     """
    #     Check if the HDF5 file is currently opened in h5py
    #
    #     Returns:
    #         bool: [True/False]
    #     """
    #     try:
    #         return self._store.is_open
    #     except AttributeError:
    #         return False

    @property
    def is_empty(self):
        """
        Check if the HDF5 file is empty

        Returns:
            bool: [True/False]
        """
        if self.file_exists:
            store = HDFStoreIO(self.file_name, mode="r")
            with store.open(mode="r"):
                len_nodes = len(eval("store.root._v_children.keys()"))
                len_groups = len(eval("store.root._v_groups.keys()"))
                return len_groups + len_nodes == 0
        else:
            return True

    @staticmethod
    def file_size(hdf):
        """
        Get size of the HDF5 file

        Args:
            hdf (FileHDFio): hdf file

        Returns:
            float: file size in Bytes
        """
        return os.path.getsize(hdf.file_name)

    def get_size(self, hdf):
        """
        Get size of the groups inside the HDF5 file

        Args:
            hdf (FileHDFio): hdf file

        Returns:
            float: file size in Bytes
        """
        return sum([sys.getsizeof(hdf[p]) for p in hdf.list_nodes()]) + sum(
            [self.get_size(hdf[p]) for p in hdf.list_groups()]
        )

    def copy(self):
        """
        Copy the Python object which links to the HDF5 file - in contrast to copy_to() which copies the content of the
        HDF5 file to a new location.

        Returns:
            FileHDFio: New FileHDFio object pointing to the same HDF5 file
        """
        new_h5 = FileHDFio(file_name=self.file_name, h5_path=self.h5_path)
        new_h5._filter = self._filter
        return new_h5

    def copy_to(self, destination, file_name=None, maintain_name=True):
        """
        Copy the content of the HDF5 file to a new location

        Args:
            destination (FileHDFio): FileHDFio object pointing to the new location
            file_name (str): name of the new HDF5 file - optional
            maintain_name (bool): by default the names of the HDF5 groups are maintained

        Returns:
            FileHDFio: FileHDFio object pointing to a file which now contains the same content as file of the current
                       FileHDFio object.
        """
        if file_name is None:
            file_name = destination.file_name
        if self.file_exists:
            with h5py.File(
                self.file_name, mode="r", libver="latest", swmr=True
            ) as f_source:
                with h5py.File(file_name, mode="a", libver="latest", swmr=True) as f_target:
                    if destination.h5_path[0] == "/":
                        dest_path = destination.h5_path[1:]
                    else:
                        dest_path = destination.h5_path
                    if maintain_name:
                        try:
                            f_target.create_group(dest_path)
                        except ValueError:
                            pass
                    if destination.is_root:
                        f_source.copy(self._h5_path, f_target)
                    else:
                        if maintain_name:
                            f_source.copy(self._h5_path, f_target[dest_path])
                        else:
                            group_name_old = self._h5_path.split("/")[-1]
                            try:
                                f_target.create_group("/tmp")
                            except ValueError:
                                pass
                            f_source.copy(self._h5_path, f_target["/tmp"])
                            try:
                                f_target.move("/tmp/" + group_name_old, dest_path)
                            except ValueError:
                                del f_target[dest_path]
                                f_target.move("/tmp/" + group_name_old, dest_path)
                            del f_target["/tmp"]
        return destination

    def create_group(self, name):
        """
        Create an HDF5 group - similar to a folder in the filesystem - the HDF5 groups allow the users to structure
        their data.

        Args:
            name (str): name of the HDF5 group

        Returns:
            FileHDFio: FileHDFio object pointing to the new group
        """
        full_name = posixpath.join(self.h5_path, name)
        with h5py.File(self.file_name, mode="a", libver="latest", swmr=True) as h:
            try:
                h.create_group(full_name)
            except ValueError:
                pass
        h_new = self[name].copy()
        return h_new

    def remove_group(self):
        """
        Remove an HDF5 group - if it exists. If the group does not exist no error message is raised.
        """
        try:
            with h5py.File(
                self.file_name, mode="a", libver="latest", swmr=True
            ) as hdf_file:
                del hdf_file[self.h5_path]
        except KeyError:
            pass

    def open(self, h5_rel_path):
        """
        Create an HDF5 group and enter this specific group. If the group exists in the HDF5 path only the h5_path is
        set correspondingly otherwise the group is created first.

        Args:
            h5_rel_path (str): relative path from the current HDF5 path - h5_path - to the new group

        Returns:
            FileHDFio: FileHDFio object pointing to the new group
        """
        new_h5_path = self.copy()
        if os.path.isabs(h5_rel_path):
            raise ValueError(
                "Absolute paths are not supported -> replace by relative path name!"
            )

        if h5_rel_path.strip() == ".":
            h5_rel_path = ""
        if h5_rel_path.strip() != "":
            new_h5_path.h5_path = posixpath.join(new_h5_path.h5_path, h5_rel_path)
        new_h5_path.history.append(h5_rel_path)

        return new_h5_path

    def close(self):
        """
        Close the current HDF5 path and return to the path before the last open
        """
        path_lst = self.h5_path.split("/")
        last = self.history[-1].strip()
        if len(last) > 0:
            hist_lst = last.split("/")
            self.h5_path = "/".join(path_lst[: -len(hist_lst)])
            if len(self.h5_path.strip()) == 0:
                self.h5_path = "/"
        del self.history[-1]

    def show_hdf(self):
        """
        Iterating over the HDF5 datastructure and generating a human readable graph.
        """
        self._walk()

    def remove_file(self):
        """
        Remove the HDF5 file with all the related content
        """
        if self.file_exists:
            os.remove(self.file_name)

    def get_from_table(self, path, name):
        """
        Get a specific value from a pandas.Dataframe

        Args:
            path (str): relative path to the data object
            name (str): parameter key

        Returns:
            dict, list, float, int: the value associated to the specific parameter key
        """
        df_table = self.get(path)
        keys = df_table["Parameter"]
        if name in keys:
            job_id = keys.index(name)
            return df_table["Value"][job_id]
        raise ValueError("Unknown name: {0}".format(name))

    def get_pandas(self, name):
        """
        Load a dictionary from the HDF5 file and display the dictionary as pandas Dataframe

        Args:
            name (str): HDF5 node name

        Returns:
            pandas.Dataframe: The dictionary is returned as pandas.Dataframe object
        """
        val = self.get(name)
        if isinstance(val, dict):
            df = pandas.DataFrame(val)
            return df

    def get(self, key):
        """
        Internal wrapper function for __getitem__() - self[name]

        Args:
            key (str, slice): path to the data or key of the data object

        Returns:
            dict, list, float, int: data or data object
        """
        return self.__getitem__(key)

    def put(self, key, value):
        """
        Store data inside the HDF5 file

        Args:
            key (str): key to store the data
            value (pandas.DataFrame, pandas.Series, dict, list, float, int): basically any kind of data is supported
        """
        self.__setitem__(key=key, value=value)

    def list_all(self):
        """
        List all groups and nodes of the HDF5 file - where groups are equivalent to directories and nodes to files.

        Returns:
            dict: {'groups': [list of groups], 'nodes': [list of nodes]}
        """
        if self.file_exists:
            store = HDFStoreIO(self.file_name, mode="r")
            with store.open(mode="r"):
                try:
                    groups = set(eval(self._h5_group + "_v_groups.keys()"))
                    nodes = set(eval(self._h5_group + "_v_children.keys()"))
                except NoSuchNodeError:
                    groups = set()
                    nodes = set()
            iopy_nodes = self._filter_io_objects(groups)
            store.close()
            return {
                "groups": sorted(list(groups - iopy_nodes)),
                "nodes": sorted(list((nodes - groups).union(iopy_nodes))),
            }
        else:
            return {"groups": [], "nodes": []}

    def list_nodes(self):
        """
        List all groups and nodes of the HDF5 file

        Returns:
            list: list of nodes
        """
        return self.list_all()["nodes"]

    def list_groups(self):
        """
        equivalent to os.listdirs (consider groups as equivalent to dirs)

        Returns:
            (list): list of groups in pytables for the path self.h5_path

        """
        return self.list_all()["groups"]

    def listdirs(self):
        """
        equivalent to os.listdirs (consider groups as equivalent to dirs)

        Returns:
            (list): list of groups in pytables for the path self.h5_path

        """
        return self.list_groups()

    def list_dirs(self):
        """
        equivalent to os.listdirs (consider groups as equivalent to dirs)

        Returns:
            (list): list of groups in pytables for the path self.h5_path
        """
        return self.list_groups()

    def keys(self):
        """
        List all groups and nodes of the HDF5 file - where groups are equivalent to directories and nodes to files.

        Returns:
            list: all groups and nodes
        """
        list_all_dict = self.list_all()
        return list_all_dict["nodes"] + list_all_dict["groups"]

    def values(self):
        """
        List all values for all groups and nodes of the HDF5 file

        Returns:
            list: list of all values
        """
        return [self[key] for key in self.keys()]

    def items(self):
        """
        List all keys and values as items of all groups and nodes of the HDF5 file

        Returns:
            list: list of sets (key, value)
        """
        return [(key, self[key]) for key in self.keys()]

    def groups(self):
        """
        Filter HDF5 file by groups

        Returns:
            FileHDFio: an HDF5 file which is filtered by groups
        """
        new = self.copy()
        new._filter = ["groups"]
        return new

    def nodes(self):
        """
        Filter HDF5 file by nodes

        Returns:
            FileHDFio: an HDF5 file which is filtered by nodes
        """
        new = self.copy()
        new._filter = ["nodes"]
        return new

    def hd_copy(self, hdf_old, hdf_new, exclude_groups=None, exclude_nodes=None):
        """
        args:
            hdf_old (ProjectHDFio): old hdf
            hdf_new (ProjectHDFio): new hdf
            exclude_groups (list/None): list of groups to delete
            exclude_nodes (list/None): list of nodes to delete
        """
        if exclude_groups is None or len(exclude_groups) == 0:
            exclude_groups_split = list()
            group_list = hdf_old.list_groups()
        else:
            exclude_groups_split = [i.split("/", 1) for i in exclude_groups]
            check_groups = [i[-1] for i in exclude_groups_split]
            group_list = list(
                (set(hdf_old.list_groups()) ^ set(check_groups))
                & set(hdf_old.list_groups())
            )

        if exclude_nodes is None or len(exclude_nodes) == 0:
            exclude_nodes_split = list()
            node_list = hdf_old.list_nodes()
        else:
            exclude_nodes_split = [i.split("/", 1) for i in exclude_nodes]
            check_nodes = [i[-1] for i in exclude_nodes_split]
            node_list = list(
                (set(hdf_old.list_nodes()) ^ set(check_nodes))
                & set(hdf_old.list_nodes())
            )
        for p in node_list:
            hdf_new[p] = hdf_old[p]
        for p in group_list:
            h_new = hdf_new.create_group(p)
            ex_n = [e[-1] for e in exclude_nodes_split if p == e[0] or len(e) == 1]
            ex_g = [e[-1] for e in exclude_groups_split if p == e[0] or len(e) == 1]
            self.hd_copy(hdf_old[p], h_new, exclude_nodes=ex_n, exclude_groups=ex_g)
        ### old ###
        # for p in hdf_old.list_nodes():
        #     if p not in exclude_nodes:
        #         hdf_new[p] = hdf_old[p]
        #
        # for p in hdf_old.list_groups():
        #     if p not in exclude_groups:
        #         h_new = hdf_new.create_group(p)
        #         self.hd_copy(hdf_old[p], h_new, exclude_groups=exclude_groups, exclude_nodes=exclude_nodes)
        return hdf_new

    def rewrite_hdf5(
        self, job_name, info=False, exclude_groups=None, exclude_nodes=None
    ):
        """
        args:
            info (True/False): whether to give the information on how much space has been saved
            exclude_groups (list/None): list of groups to delete from hdf
            exclude_nodes (list/None): list of nodes to delete from hdf
        """
        # hdf = self._hdf5
        if exclude_groups is None:
            exclude_groups = ["interactive"]
        file_name = self.file_name
        _path = file_name.split("/")[-1]
        _path = ".".join(_path.split(".")[:-1])
        # path = '/'.join(p_lst[:-1])
        new_file = _path + "_rewrite"

        hdf_new = ProjectHDFio(
            project=self.project, file_name=new_file, h5_path="/" + job_name
        )
        hdf_new = self.hd_copy(
            self, hdf_new, exclude_groups=exclude_groups, exclude_nodes=exclude_nodes
        )

        if info:
            print("job: {}".format(job_name))
            print(
                "compression rate from old to new: {}".format(
                    self.file_size(self) / self.file_size(hdf_new)
                )
            )
            print(
                "data size vs file size: {}".format(
                    self.get_size(hdf_new) / self.file_size(hdf_new)
                )
            )
        self.remove_file()
        os.rename(hdf_new.file_name, file_name)

    def __setitem__(self, key, value):
        """
        Store data inside the HDF5 file

        Args:
            key (str): key to store the data
            value (pandas.DataFrame, pandas.Series, dict, list, float, int): basically any kind of data is supported
        """
        if hasattr(value, "to_hdf") & (
            not isinstance(value, (pandas.DataFrame, pandas.Series))
        ):
            value.to_hdf(self, key)
        elif (
            isinstance(value, (list, np.ndarray))
            and len(value) > 0
            and isinstance(value[0], (list, np.ndarray))
            and len(value[0]) > 0
            and not isinstance(value[0][0], str)
        ):
            shape_lst = [np.shape(sub) for sub in value]
            if all([shape_lst[0][1:] == t[1:] for t in shape_lst]):
                h5io.write_hdf5(
                    self.file_name,
                    np.array([np.array(v) for v in value]),
                    title=posixpath.join(self.h5_path, key),
                    overwrite="update",
                    use_json=False,
                )
            else:
                h5io.write_hdf5(
                    self.file_name,
                    value,
                    title=posixpath.join(self.h5_path, key),
                    overwrite="update",
                    use_json=True,
                )
        elif isinstance(value, tuple):
            h5io.write_hdf5(
                self.file_name,
                list(value),
                title=posixpath.join(self.h5_path, key),
                overwrite="update",
                use_json=True,
            )
        else:
            h5io.write_hdf5(
                self.file_name,
                value,
                title=posixpath.join(self.h5_path, key),
                overwrite="update",
                use_json=True,
            )

    def __delitem__(self, key):
        """
        Delete item from the HDF5 file

        Args:
            key (str): key of the item to delete
        """
        if self.file_exists:
            try:
                store = HDFStoreIO(self.file_name, mode="a")
                with store.open(mode="a"):
                    del store[key]
            except (AttributeError, KeyError, HDF5ExtError):
                pass

    def __str__(self):
        """
        Machine readable string representation

        Returns:
            str: list all nodes and groups as string
        """
        return self.__repr__()

    def __repr__(self):
        """
        Human readable string representation

        Returns:
            str: list all nodes and groups as string
        """
        return str(self.list_all())

    def __del__(self):
        del self._file_name
        del self.history
        del self._h5_path
        del self._h5_group

    def __enter__(self):
        """
        Compatibility function for the with statement
        """
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        """
        Compatibility function for the with statement
        """
        self.close()
        try:
            self._store.close()
        except AttributeError:
            pass

    def __getitem__(self, item):
        """
        Get/ read data from the HDF5 file

        Args:
            item (str, slice): path to the data or key of the data object

        Returns:
            dict, list, float, int: data or data object
        """
        if isinstance(item, slice):
            if not (item.start or item.stop or item.step):
                return self.values()
            raise NotImplementedError("Implement if needed, e.g. for [:]")
        else:
            item_lst = item.split("/")
            if len(item_lst) == 1 and item_lst[0] != "..":
                if item in self.list_nodes():
                    obj = h5io.read_hdf5(self.file_name, title=self._get_h5_path(item))
                    return obj
                if item in self.list_groups():
                    with self.open(item) as hdf_item:
                        obj = hdf_item.copy()
                        return obj
                raise ValueError("Unknown item: {}".format(item))
            else:
                if item_lst[0] == "":  # absoute HDF5 path
                    item_abs_lst = os.path.normpath(item).replace("\\", "/").split("/")
                else:  # relative HDF5 path
                    item_abs_lst = (
                        os.path.normpath(os.path.join(self.h5_path, item))
                        .replace("\\", "/")
                        .split("/")
                    )
                    # print(item, item_abs_lst)
                    # item_abs_lst = os.path.normpath(os.path.join(self.h5_path, item)).replace('\\', '/').split("/")
                if (
                    item_abs_lst[1] == "" and len(item_abs_lst) == 2
                ):  # leaving the HDF5 file
                    return self._create_project_from_hdf5()
                elif item_abs_lst[1] == "":
                    return self._create_project_from_hdf5()["/".join(item_abs_lst[2:])]
                else:
                    hdf_object = self.copy()
                    hdf_object.h5_path = "/".join(item_abs_lst[:-1])
                    return hdf_object[item_abs_lst[-1]]

    def _read(self, item):
        """
        Internal read function to read data from the HDF5 file

        Args:
            item (str): path to the data or key of the data object

        Returns:
            dict, list, float, int: data or data object
        """
        return h5io.read_hdf5(self.file_name, title=self._get_h5_path(item))

    # def _open_store(self, mode="r"):
    #     """
    #     Internal function to open the HDF5 file
    #
    #     Args:
    #         mode (str): file mode can be either 'w': write, 'r': read or 'a': append
    #     """
    #     try:
    #         if not self._store:
    #             self._store = HDFStoreIO(self.file_name, mode=mode)
    #     except AttributeError:
    #         self._store = HDFStoreIO(self.file_name, mode=mode)
    #
    # def _close_store(self):
    #     """
    #     Internal function to close the HDF5 file
    #     """
    #     try:
    #         self._store.close()
    #         self._store = None
    #     except AttributeError:
    #         pass

    def _create_project_from_hdf5(self):
        """
        Internal function to create a pyiron project pointing to the directory where the HDF5 file is located.

        Returns:
            Project: pyiron project object
        """
        from pyiron.base.project.generic import Project

        return Project(path=self.file_path)

    def _get_h5_path(self, name):
        """
        Internal function to combine the current h5_path with the relative path

        Args:
            name (str): relative path

        Returns:
            str: combined path
        """
        return posixpath.join(self.h5_path, name)

    def _get_h5io_type(self, name):
        """
        Internal function to get h5io type

        Args:
            name (str): HDF5 key

        Returns:
            str: h5io type
        """
        store = HDFStoreIO(self.file_name, mode="r")
        with store.open(mode="r"):
            return str(eval("".join([self._h5_group, name, "._v_title"])))

    def _filter_io_objects(self, groups):
        """
        Internal function to extract h5io objects (which have the same type as normal groups)

        Args:
            groups (list, set): list of groups (as obtained e.g. from listdirs

        Returns:
            set: h5io objects
        """
        h5io_types = (
            "dict",
            "list",
            "tuple",
            "pd_dataframe",
            "pd_series",
            "multiarray",
            "json",
        )
        group_h5io = set(
            [group for group in groups if self._get_h5io_type(group) in h5io_types]
        )
        return group_h5io

    def _walk(self, level=0):
        """
        Internal helper function for show_hdf() - iterating over the HDF5 datastructure and generating a human readable
        graph.

        Args:
            level (int): iteration level
        """
        l_dict = self.list_all()
        indent = level * "  "
        for node in l_dict["nodes"]:
            print(indent + "node", node)
        for group in l_dict["groups"]:
            print(indent + "group: ", group)
            with self.open(group) as hdf_group:
                hdf_group._walk(level=level + 1)


class ProjectHDFio(FileHDFio):
    """
    The ProjectHDFio class connects the FileHDFio and the Project class, it is derived from the FileHDFio class but in
    addition the a project object instance is located at self.project enabling direct access to the database and other
    project related functionality, some of which are mapped to the ProjectHDFio class as well.

    Args:
        project (Project): pyiron Project the current HDF5 project is located in
        file_name (str): name of the HDF5 file - in contrast to the FileHDFio object where file_name represents the
                         absolute path of the HDF5 file.
        h5_path (str): absolute path inside the h5 path - starting from the root group
        mode (str): mode : {'a', 'w', 'r', 'r+'}, default 'a'
                    See HDFStore docstring or tables.open_file for info about modes

    Attributes:

        .. attribute:: project

            Project instance the ProjectHDFio object is located in

        .. attribute:: root_path

            the pyiron user directory, defined in the .pyiron configuration

        .. attribute:: project_path

            the relative path of the current project / folder starting from the root path
            of the pyiron user directory

        .. attribute:: path

            the absolute path of the current project / folder plus the absolute path in the HDF5 file as one path

        .. attribute:: file_name

            absolute path to the HDF5 file

        .. attribute:: h5_path

            path inside the HDF5 file - also stored as absolute path

        .. attribute:: history

            previously opened groups / folders

        .. attribute:: file_exists

            boolean if the HDF5 was already written

        .. attribute:: base_name

            name of the HDF5 file but without any file extension

        .. attribute:: file_path

            directory where the HDF5 file is located

        .. attribute:: is_root

            boolean if the HDF5 object is located at the root level of the HDF5 file

        .. attribute:: is_open

            boolean if the HDF5 file is currently opened - if an active file handler exists

        .. attribute:: is_empty

            boolean if the HDF5 file is empty

        .. attribute:: user

            current unix/linux/windows user who is running pyiron

        .. attribute:: sql_query

            an SQL query to limit the jobs within the project to a subset which matches the SQL query.

        .. attribute:: db

            connection to the SQL database

        .. attribute:: working_directory

            working directory of the job is executed in - outside the HDF5 file
    """

    def __init__(self, project, file_name, h5_path=None, mode=None):
        self._file_name = file_name.replace("\\", "/")
        if ".h5" not in file_name:
            self._file_name += ".h5"
        if not h5_path:
            h5_path = "/"
        self._project = project.copy()
        super(ProjectHDFio, self).__init__(
            file_name=os.path.join(self._project.path, self._file_name).replace(
                "\\", "/"
            ),
            h5_path=h5_path,
            mode=mode,
        )

    @property
    def base_name(self):
        """
        The absolute path to of the current pyiron project - absolute path on the file system, not including the HDF5
        path.

        Returns:
            str: current project path
        """
        return self._project.path

    @property
    def db(self):
        """
        Get connection to the SQL database

        Returns:
            DatabaseAccess: database conncetion
        """
        return self._project.db

    @property
    def path(self):
        """
        Absolute path of the HDF5 group starting from the system root - combination of the absolute system path plus the
        absolute path inside the HDF5 file starting from the root group.

        Returns:
            str: absolute path
        """
        return os.path.join(self._project.path, self.h5_path[1:]).replace("\\", "/")

    @property
    def project(self):
        """
        Get the project instance the ProjectHDFio object is located in

        Returns:
            Project: pyiron project
        """
        return self._project

    @property
    def project_path(self):
        """
        the relative path of the current project / folder starting from the root path
        of the pyiron user directory

        Returns:
            str: relative path of the current project / folder
        """
        return self._project.project_path

    @property
    def root_path(self):
        """
        the pyiron user directory, defined in the .pyiron configuration

        Returns:
            str: pyiron user directory of the current project
        """
        return self._project.root_path

    @property
    def sql_query(self):
        """
        Get the SQL query for the project

        Returns:
            str: SQL query
        """
        return self._project.sql_query

    @sql_query.setter
    def sql_query(self, new_query):
        """
        Set the SQL query for the project

        Args:
            new_query (str): SQL query
        """
        self._project.sql_query = new_query

    @property
    def user(self):
        """
        Get current unix/linux/windows user who is running pyiron

        Returns:
            str: username
        """
        return self._project.user

    @property
    def working_directory(self):
        """
        Get the working directory of the current ProjectHDFio object. The working directory equals the path but it is
        represented by the filesystem:
            /absolute/path/to/the/file.h5/path/inside/the/hdf5/file
        becomes:
            /absolute/path/to/the/file_hdf5/path/inside/the/hdf5/file

        Returns:
            str: absolute path to the working directory
        """
        project_full_path = "/".join(self.file_name.split("/")[:-1])
        file_name = self.file_name.split("/")[-1]
        if ".h5" in file_name:
            file_name = file_name.split(".h5")[0]
        file_name += "_hdf5"
        if self.h5_path[0] == "/":
            h5_path = self.h5_path[1:]
        else:
            h5_path = self.h5_path
        return posixpath.join(project_full_path, file_name, h5_path)

    @property
    def _filter(self):
        """
        Get project filter

        Returns:
            str: project filter
        """
        return self._project._filter

    @_filter.setter
    def _filter(self, new_filter):
        """
        Set project filter

        Args:
            new_filter (str): project filter
        """
        self._project._filter = new_filter

    @property
    def _inspect_mode(self):
        """
        Check if inspect mode is activated

        Returns:
            bool: [True/False]
        """
        return self._project._inspect_mode

    @_inspect_mode.setter
    def _inspect_mode(self, read_mode):
        """
        Activate or deactivate inspect mode

        Args:
            read_mode (bool): [True/False]
        """
        self._project._inspect_mode = read_mode

    @property
    def name(self):
        return os.path.basename(self.h5_path)

    def copy(self):
        """
        Copy the ProjectHDFio object - copying just the Python object but maintaining the same pyiron path

        Returns:
            ProjectHDFio: copy of the ProjectHDFio object
        """
        new_h5 = ProjectHDFio(
            project=self._project, file_name=self._file_name, h5_path=self._h5_path
        )
        new_h5._filter = self._filter
        return new_h5

    def create_hdf(self, path, job_name):
        """
        Create an ProjectHDFio object to store project related information - for testing aggregated data

        Args:
            path (str): absolute path
            job_name (str): name of the HDF5 container

        Returns:
            ProjectHDFio: HDF5 object
        """
        return self._project.create_hdf(path=path, job_name=job_name)

    def create_working_directory(self):
        """
        Create the working directory on the file system if it does not exist already.
        """
        if not os.path.isdir(self.working_directory):
            os.makedirs(self.working_directory)

    def create_object(self, class_name, **qwargs):
        """
        Internal function to create a pyiron object

        Args:
            class_name (str): name of a pyiron class
            **qwargs: object parameters

        Returns:
            pyiron object: defined by the pyiron class in class_name with the input from **qwargs
        """
        class_name = class_name.split(".")[-1][:-2]
        if class_name in self._project.job_type.job_class_dict.keys():
            return getattr(
                importlib.import_module(
                    self._project.job_type.job_class_dict[class_name]
                ),
                class_name,
            )(**qwargs)

    def to_object(self, object_type=None, **qwargs):
        """
        Load the full pyiron object from an HDF5 file

        Args:
            object_type: if the 'TYPE' node is not available in the HDF5 file a manual object type can be set - optional
            **qwargs: optional parameters ['job_name', 'project'] - to specify the location of the HDF5 path

        Returns:
            GenericJob: pyiron object
        """
        if "job_name" not in qwargs:
            qwargs["job_name"] = self.file_name.split("/")[-1].split(".h5")[0]
        if "project" not in qwargs:
            qwargs["project"] = self.__class__(
                project=self._create_project_from_hdf5(), file_name=qwargs["job_name"]
            )
        if "TYPE" not in self.list_nodes():
            if object_type is None:
                raise ValueError(
                    "Objects can be only recovered from hdf5 if TYPE is given"
                )
            else:
                obj_type = object_type
        else:
            obj_type = self.get("TYPE")
            if object_type is not None:
                if object_type != obj_type:
                    raise ValueError(
                        "Object type in hdf5-file must be identical to input parameter"
                    )
        new_obj = self.create_object(obj_type, **qwargs)
        if obj_type != str(type(new_obj)):  # Backwards compatibility
            self["TYPE"] = str(type(new_obj))
        new_obj.from_hdf()
        return new_obj

    def get_job_id(self, job_specifier):
        """
        get the job_id for job named job_name in the local project path from database

        Args:
            job_specifier (str, int): name of the job or job ID

        Returns:
            int: job ID of the job
        """
        return self._project.get_job_id(job_specifier=job_specifier)

    def inspect(self, job_specifier):
        """
        Inspect an existing pyiron object - most commonly a job - from the database

        Args:
            job_specifier (str, int): name of the job or job ID

        Returns:
            JobCore: Access to the HDF5 object - not a GenericJob object - use load() instead.
        """
        return self._project.inspect(job_specifier=job_specifier)

    def load(self, job_specifier, convert_to_object=True):
        """
        Load an existing pyiron object - most commonly a job - from the database

        Args:
            job_specifier (str, int): name of the job or job ID
            convert_to_object (bool): convert the object to an pyiron object or only access the HDF5 file - default=True
                                      accessing only the HDF5 file is about an order of magnitude faster, but only
                                      provides limited functionality. Compare the GenericJob object to JobCore object.

        Returns:
            GenericJob, JobCore: Either the full GenericJob object or just a reduced JobCore object
        """
        return self._project.load(
            job_specifier=job_specifier, convert_to_object=convert_to_object
        )

    def load_from_jobpath(self, job_id=None, db_entry=None, convert_to_object=True):
        """
        Internal function to load an existing job either based on the job ID or based on the database entry dictionary.

        Args:
            job_id (int): Job ID - optional, but either the job_id or the db_entry is required.
            db_entry (dict): database entry dictionary - optional, but either the job_id or the db_entry is required.
            convert_to_object (bool): convert the object to an pyiron object or only access the HDF5 file - default=True
                                      accessing only the HDF5 file is about an order of magnitude faster, but only
                                      provides limited functionality. Compare the GenericJob object to JobCore object.

        Returns:
            GenericJob, JobCore: Either the full GenericJob object or just a reduced JobCore object
        """
        return self._project.load_from_jobpath(
            job_id=job_id, db_entry=db_entry, convert_to_object=convert_to_object
        )

    def remove_job(self, job_specifier, _unprotect=False):
        """
        Remove a single job from the project based on its job_specifier - see also remove_jobs()

        Args:
            job_specifier (str, int): name of the job or job ID
            _unprotect (bool): [True/False] delete the job without validating the dependencies to other jobs
                               - default=False
        """
        self._project.remove_job(job_specifier=job_specifier, _unprotect=_unprotect)

    def _create_project_from_hdf5(self):
        """
        Internal function to create a pyiron project pointing to the directory where the HDF5 file is located.

        Returns:
            Project: pyiron project object
        """
        return self._project.__class__(path=self.file_path)
