# fileio_backends.py
#
# This file is part of scqubits.
#
#    Copyright (c) 2019, <NAME> and <NAME>
#    All rights reserved.
#
#    This source code is licensed under the BSD-style license found in the
#    LICENSE file in the root directory of this source tree.
############################################################################
"""
Helper routines for writing data to h5 files.
"""
import ast
import csv
import os
import re
from abc import ABC, abstractmethod

import numpy as np

try:
    import h5py
except ImportError:
    _HAS_H5PY = False
else:
    _HAS_H5PY = True

import scqubits.io_utils.fileio as io
import scqubits.utils.misc as utils


class IOWriter(ABC):
    """
    ABC for writing class instance data to file.

    Parameters
    ----------
    filename: str
    file_handle: h5.Group, optional
    """
    def __init__(self, filename, file_handle=None):
        self.filename = filename
        self.io_data = None
        self.file_handle = file_handle

    @abstractmethod
    def to_file(self, io_data, **kwargs):
        pass

    @abstractmethod
    def write_attributes(self, *args, **kwargs):
        pass

    @abstractmethod
    def write_ndarrays(self, *args, **kwargs):
        pass

    @abstractmethod
    def write_objects(self, *args, **kwargs):
        pass


class H5Writer(IOWriter):
    """Writes IOData to a custom-format h5 file"""
    def write_attributes(self, h5file_group):
        """
        Attribute data consists of

         1. `__init__` parameters that are of type str or numerical. These are directly written into `h5py.Group.attrs`
         2. lists are stored under `<h5py.Group>/__lists`
         3. dicts are stored under `<h5py.Group>/__dicts`

        Parameters
        ----------
        h5file_group: h5py.Group
        """
        h5file_group.attrs.create("__type", self.io_data.typename)    # Record the type of the current class instance
        attributes = self.io_data.attributes
        for attr_name, attr_value in attributes.items():
            if isinstance(attr_value, dict):  # h5py does not serialize dicts automatically, so have to do it manually
                group_name = "__dicts/" + attr_name
                h5file_group.create_group(group_name)
                io.write(attr_value, self.filename, file_handle=h5file_group[group_name])
            elif isinstance(attr_value, (list, tuple)):
                group_name = "__lists/" + attr_name
                h5file_group.create_group(group_name)
                io.write(attr_value, self.filename, file_handle=h5file_group[group_name])
            else:
                h5file_group.attrs[attr_name] = attr_value

    def write_ndarrays(self, h5file_group):
        """
        Writes ndarray (float or complex) data contained in `self.iodata` to the provided `h5py.Group` as a
        `h5py.Dataset`, using gzip compression.

        Parameters
        ----------
        h5file_group: h5py.Group
        """
        for name, array in self.io_data.ndarrays.items():
            h5file_group.create_dataset(name, data=array, dtype=array.dtype, compression="gzip")

    def write_objects(self, h5file_group):
        """
        Writes data representing a Python object other than ndarray, list and dict, contained in `self.iodata` to the
        provided `h5py.Group`  und `<h5py.Group>/__objects`.

        Parameters
        ----------
        h5file_group: h5py.Group
        """
        h5file_group = h5file_group.create_group("__objects")
        for obj_name in self.io_data.objects.keys():
            new_h5group = h5file_group.create_group(obj_name)
            io.write(self.io_data.objects[obj_name], self.filename, file_handle=new_h5group)

    @utils.Required(h5py=_HAS_H5PY)
    def to_file(self, io_data, file_handle=None):
        """
        Takes the serialized IOData and writes it either to a new h5 file with file name given by `self.filename` to to
        the given h5py.Group of an open h5 file.

        Parameters
        ----------
        io_data: IOData
        file_handle: h5py.Group, optional
        """
        self.io_data = io_data
        if file_handle is None:
            h5file_group = h5py.File(self.filename, 'w')
        else:
            h5file_group = file_handle

        self.write_attributes(h5file_group)
        self.write_ndarrays(h5file_group)
        self.write_objects(h5file_group)


class H5Reader:
    """
    Enables reading h5 files generated with scqubits.

    Parameters
    ----------
    filename: str
    file_handle: h5py.Group, optional
    """
    def __init__(self, filename, file_handle=None):
        self.filename = filename
        self.io_data = None
        self.file_handle = file_handle

    @staticmethod
    def h5_attrs_to_dict(h5_attrs):
        """
        Converts h5 attribute data to a Python dictionary.

        Parameters
        ----------
        h5_attrs: h5py.AttributeManager
            as obtained by accessing `<h5py.Group>.attrs`

        Returns
        -------
        dict [str, str or Number]
        """
        return {attr_name: attr_value for attr_name, attr_value in h5_attrs.items()}

    def read_attributes(self, h5file_group):
        """
        Read data from h5 file group that is stored directly as `<h5py.Group>.attrs`, or saved in subgroups titled
        `<h5py.Group>/__lists` and `<h5py.Group>/__dicts`.

        Parameters
        ----------
        h5file_group: h5py.Group

        Returns
        -------
        dict [str, dict or list]
        """
        attributes = self.h5_attrs_to_dict(h5file_group.attrs)
        if '__dicts' in h5file_group:
            for dict_name in h5file_group['__dicts']:
                attributes[dict_name] = io.read(self.filename, h5file_group['__dicts/' + dict_name])
        if '__lists' in h5file_group:
            for list_name in h5file_group['__lists']:
                attributes[list_name] = io.read(self.filename, h5file_group['__lists/' + list_name])
        return attributes

    def read_ndarrays(self, h5file_group):
        """
        Read numpy array data from h5 file group.

        Parameters
        ----------
        h5file_group: h5py.Group

        Returns
        -------
        dict [str, ndarray]
        """
        ndarrays = {name: array[:] for name, array in h5file_group.items() if isinstance(array, h5py.Dataset)}
        return ndarrays

    def read_objects(self, h5file_group):
        """
        Read data from the given h5 file group that represents a Python object other than an ndarray, list, or dict.

        Parameters
        ----------
        h5file_group: h5py.Group

        Returns
        -------
        dict [str, IOData]
        """
        inner_objects = {}
        h5file_group = h5file_group["__objects"]
        for obj_name in h5file_group:
            inner_objects[obj_name] = io.read(self.filename, h5file_group[obj_name])
        return inner_objects

    @utils.Required(h5py=_HAS_H5PY)
    def from_file(self, filename, file_handle=None):
        """
        Either opens a new h5 file for reading or accesses an already opened file via the given h5.Group handle. Reads
        all data from the three categories of attributes (incl. lists and dicts), ndarrays, and objects.

        Parameters
        ----------
        filename: str
        file_handle: h5.Group, optional

        Returns
        -------
        IOData
        """
        if file_handle is None:
            h5file_group = h5py.File(filename, 'r')
        else:
            h5file_group = file_handle

        attributes = self.read_attributes(h5file_group)
        typename = attributes['__type']
        del attributes['__type']
        ndarrays = self.read_ndarrays(h5file_group)
        inner_objects = self.read_objects(h5file_group)
        return io.IOData(typename, attributes, ndarrays, inner_objects)


class CSVWriter(IOWriter):
    """
    Given filename='somename.csv', write initdata into somename.csv
    Then, additional csv files are written for each dataset, with filenames: 'somename_' + dataname0 + '.csv' etc.
    """
    def append_ndarray_info(self, attributes):
        """Add data set information to attributes, so that dataset names and dimensions are available
        in attributes CSV file."""
        for index, dataname in enumerate(self.io_data.ndarrays.keys()):
            data = self.io_data.ndarrays[dataname]
            attributes['dataset' + str(index)] = dataname

            if data.ndim == 3:
                slice_count = len(data)
            else:
                slice_count = 1
            attributes['dataset' + str(index) + '.slices'] = slice_count
        return attributes

    def write_attributes(self, filename):
        attributes = self.io_data.attributes
        attributes["__type"] = self.io_data.typename
        attributes = self.append_ndarray_info(attributes)
        with open(filename, mode='w', newline='') as meta_file:
            file_writer = csv.writer(meta_file, delimiter=',')
            file_writer.writerow(attributes.keys())
            file_writer.writerow(attributes.values())

    def write_ndarrays(self, filename):
        filename_stub, _ = os.path.splitext(filename)
        for dataname, dataset in self.io_data.ndarrays.items():
            filename = filename_stub + '_' + dataname + '.csv'
            self.write_data(filename, dataset)

    def write_data(self, filename, dataset):
        if dataset.ndim <= 2:
            np.savetxt(filename, dataset)
        elif dataset.ndim == 3:
            np_savetxt_3d(dataset, filename)
        else:
            raise Exception("Dataset has dimensions > 3. Cannot write to CSV file.")

    def write_objects(self, *args, **kwargs):
        raise NotImplementedError

    def to_file(self, io_data, **kwargs):
        self.io_data = io_data
        self.write_attributes(self.filename)
        self.write_ndarrays(self.filename)
        # no support for write_objects in CSV format


class CSVReader:
    @staticmethod
    def read_attributes(filename):
        with open(filename, mode='r') as meta_file:
            file_reader = csv.reader(meta_file, delimiter=',')
            meta_keys = file_reader.__next__()
            meta_values = file_reader.__next__()
        return dict(zip(meta_keys, meta_values))

    def process_metadict(self, meta_dict):
        attributes = {
            attr_name: utils.to_expression_or_string(attr_value) for attr_name, attr_value in meta_dict.items()
            if not re.match(r'dataset\d+', attr_name)
        }
        data_names = [dataname for datalabel, dataname in meta_dict.items() if re.match(r'dataset\d+$', datalabel)]
        data_slices = [ast.literal_eval(value) for key, value in meta_dict.items()
                       if re.match(r'dataset\d+.slices', key)]
        return attributes, data_names, data_slices

    @staticmethod
    def read_data(filename, slices):
        try:
            data_array = np.loadtxt(filename)
        except ValueError:
            data_array = np.loadtxt(filename, dtype=np.complex_)
        if slices > 1:
            nrows, ncols = data_array.shape
            return data_array.reshape((slices, nrows//slices, ncols))
        return data_array

    def from_file(self, filename, **kwargs):
        """
        Parameters
        ----------
        filename: str

        Returns
        -------
        class instance generated from file data
        """
        ext_attributes = self.read_attributes(filename)
        typename = ext_attributes['__type']
        del ext_attributes['__type']

        attributes, data_names, data_slices = self.process_metadict(ext_attributes)

        filename_stub, _ = os.path.splitext(filename)
        ndarrays = {}
        for index, dataname in enumerate(data_names):
            data_filename = filename_stub + '_' + dataname + '.csv'
            slices = data_slices[index]
            ndarrays[dataname] = self.read_data(data_filename, slices)

        return io.IOData(typename, attributes, ndarrays, objects=None)


def np_savetxt_3d(array3d, filename):
    """
    Helper function that splits a 3d numpy array into 2d slices for writing as csv data to a new file. Slices are
    separated by a comment row `# New slice`.

    Parameters
    ----------
    array3d: ndarray with ndim = 3
    filename: str
    """
    with open(filename, mode='w', newline='') as datafile:
        datafile.write('# Array shape: {0}\n'.format(array3d.shape))
        for data_slice in array3d:
            np.savetxt(datafile, data_slice)
            datafile.write('# New slice\n')

# fileio_backends.py
#
# This file is part of scqubits.
#
#    Copyright (c) 2019, <NAME> and <NAME>
#    All rights reserved.
#
#    This source code is licensed under the BSD-style license found in the
#    LICENSE file in the root directory of this source tree.
############################################################################
"""
Helper routines for writing data to h5 files.
"""
import ast
import csv
import os
import re
from abc import ABC, abstractmethod

import numpy as np

try:
    import h5py
except ImportError:
    _HAS_H5PY = False
else:
    _HAS_H5PY = True

import scqubits.io_utils.fileio as io
import scqubits.utils.misc as utils


class IOWriter(ABC):
    """
    ABC for writing class instance data to file.

    Parameters
    ----------
    filename: str
    file_handle: h5.Group, optional
    """
    def __init__(self, filename, file_handle=None):
        self.filename = filename
        self.io_data = None
        self.file_handle = file_handle

    @abstractmethod
    def to_file(self, io_data, **kwargs):
        pass

    @abstractmethod
    def write_attributes(self, *args, **kwargs):
        pass

    @abstractmethod
    def write_ndarrays(self, *args, **kwargs):
        pass

    @abstractmethod
    def write_objects(self, *args, **kwargs):
        pass


class H5Writer(IOWriter):
    """Writes IOData to a custom-format h5 file"""
    def write_attributes(self, h5file_group):
        """
        Attribute data consists of

         1. `__init__` parameters that are of type str or numerical. These are directly written into `h5py.Group.attrs`
         2. lists are stored under `<h5py.Group>/__lists`
         3. dicts are stored under `<h5py.Group>/__dicts`

        Parameters
        ----------
        h5file_group: h5py.Group
        """
        h5file_group.attrs.create("__type", self.io_data.typename)    # Record the type of the current class instance
        attributes = self.io_data.attributes
        for attr_name, attr_value in attributes.items():
            if isinstance(attr_value, dict):  # h5py does not serialize dicts automatically, so have to do it manually
                group_name = "__dicts/" + attr_name
                h5file_group.create_group(group_name)
                io.write(attr_value, self.filename, file_handle=h5file_group[group_name])
            elif isinstance(attr_value, (list, tuple)):
                group_name = "__lists/" + attr_name
                h5file_group.create_group(group_name)
                io.write(attr_value, self.filename, file_handle=h5file_group[group_name])
            else:
                h5file_group.attrs[attr_name] = attr_value

    def write_ndarrays(self, h5file_group):
        """
        Writes ndarray (float or complex) data contained in `self.iodata` to the provided `h5py.Group` as a
        `h5py.Dataset`, using gzip compression.

        Parameters
        ----------
        h5file_group: h5py.Group
        """
        for name, array in self.io_data.ndarrays.items():
            h5file_group.create_dataset(name, data=array, dtype=array.dtype, compression="gzip")

    def write_objects(self, h5file_group):
        """
        Writes data representing a Python object other than ndarray, list and dict, contained in `self.iodata` to the
        provided `h5py.Group`  und `<h5py.Group>/__objects`.

        Parameters
        ----------
        h5file_group: h5py.Group
        """
        h5file_group = h5file_group.create_group("__objects")
        for obj_name in self.io_data.objects.keys():
            new_h5group = h5file_group.create_group(obj_name)
            io.write(self.io_data.objects[obj_name], self.filename, file_handle=new_h5group)

    @utils.Required(h5py=_HAS_H5PY)
    def to_file(self, io_data, file_handle=None):
        """
        Takes the serialized IOData and writes it either to a new h5 file with file name given by `self.filename` to to
        the given h5py.Group of an open h5 file.

        Parameters
        ----------
        io_data: IOData
        file_handle: h5py.Group, optional
        """
        self.io_data = io_data
        if file_handle is None:
            h5file_group = h5py.File(self.filename, 'w')
        else:
            h5file_group = file_handle

        self.write_attributes(h5file_group)
        self.write_ndarrays(h5file_group)
        self.write_objects(h5file_group)


class H5Reader:
    """
    Enables reading h5 files generated with scqubits.

    Parameters
    ----------
    filename: str
    file_handle: h5py.Group, optional
    """
    def __init__(self, filename, file_handle=None):
        self.filename = filename
        self.io_data = None
        self.file_handle = file_handle

    @staticmethod
    def h5_attrs_to_dict(h5_attrs):
        """
        Converts h5 attribute data to a Python dictionary.

        Parameters
        ----------
        h5_attrs: h5py.AttributeManager
            as obtained by accessing `<h5py.Group>.attrs`

        Returns
        -------
        dict [str, str or Number]
        """
        return {attr_name: attr_value for attr_name, attr_value in h5_attrs.items()}

    def read_attributes(self, h5file_group):
        """
        Read data from h5 file group that is stored directly as `<h5py.Group>.attrs`, or saved in subgroups titled
        `<h5py.Group>/__lists` and `<h5py.Group>/__dicts`.

        Parameters
        ----------
        h5file_group: h5py.Group

        Returns
        -------
        dict [str, dict or list]
        """
        attributes = self.h5_attrs_to_dict(h5file_group.attrs)
        if '__dicts' in h5file_group:
            for dict_name in h5file_group['__dicts']:
                attributes[dict_name] = io.read(self.filename, h5file_group['__dicts/' + dict_name])
        if '__lists' in h5file_group:
            for list_name in h5file_group['__lists']:
                attributes[list_name] = io.read(self.filename, h5file_group['__lists/' + list_name])
        return attributes

    def read_ndarrays(self, h5file_group):
        """
        Read numpy array data from h5 file group.

        Parameters
        ----------
        h5file_group: h5py.Group

        Returns
        -------
        dict [str, ndarray]
        """
        ndarrays = {name: array[:] for name, array in h5file_group.items() if isinstance(array, h5py.Dataset)}
        return ndarrays

    def read_objects(self, h5file_group):
        """
        Read data from the given h5 file group that represents a Python object other than an ndarray, list, or dict.

        Parameters
        ----------
        h5file_group: h5py.Group

        Returns
        -------
        dict [str, IOData]
        """
        inner_objects = {}
        h5file_group = h5file_group["__objects"]
        for obj_name in h5file_group:
            inner_objects[obj_name] = io.read(self.filename, h5file_group[obj_name])
        return inner_objects

    @utils.Required(h5py=_HAS_H5PY)
    def from_file(self, filename, file_handle=None):
        """
        Either opens a new h5 file for reading or accesses an already opened file via the given h5.Group handle. Reads
        all data from the three categories of attributes (incl. lists and dicts), ndarrays, and objects.

        Parameters
        ----------
        filename: str
        file_handle: h5.Group, optional

        Returns
        -------
        IOData
        """
        if file_handle is None:
            h5file_group = h5py.File(filename, 'r')
        else:
            h5file_group = file_handle

        attributes = self.read_attributes(h5file_group)
        typename = attributes['__type']
        del attributes['__type']
        ndarrays = self.read_ndarrays(h5file_group)
        inner_objects = self.read_objects(h5file_group)
        return io.IOData(typename, attributes, ndarrays, inner_objects)


class CSVWriter(IOWriter):
    """
    Given filename='somename.csv', write initdata into somename.csv
    Then, additional csv files are written for each dataset, with filenames: 'somename_' + dataname0 + '.csv' etc.
    """
    def append_ndarray_info(self, attributes):
        """Add data set information to attributes, so that dataset names and dimensions are available
        in attributes CSV file."""
        for index, dataname in enumerate(self.io_data.ndarrays.keys()):
            data = self.io_data.ndarrays[dataname]
            attributes['dataset' + str(index)] = dataname

            if data.ndim == 3:
                slice_count = len(data)
            else:
                slice_count = 1
            attributes['dataset' + str(index) + '.slices'] = slice_count
        return attributes

    def write_attributes(self, filename):
        attributes = self.io_data.attributes
        attributes["__type"] = self.io_data.typename
        attributes = self.append_ndarray_info(attributes)
        with open(filename, mode='w', newline='') as meta_file:
            file_writer = csv.writer(meta_file, delimiter=',')
            file_writer.writerow(attributes.keys())
            file_writer.writerow(attributes.values())

    def write_ndarrays(self, filename):
        filename_stub, _ = os.path.splitext(filename)
        for dataname, dataset in self.io_data.ndarrays.items():
            filename = filename_stub + '_' + dataname + '.csv'
            self.write_data(filename, dataset)

    def write_data(self, filename, dataset):
        if dataset.ndim <= 2:
            np.savetxt(filename, dataset)
        elif dataset.ndim == 3:
            np_savetxt_3d(dataset, filename)
        else:
            raise Exception("Dataset has dimensions > 3. Cannot write to CSV file.")

    def write_objects(self, *args, **kwargs):
        raise NotImplementedError

    def to_file(self, io_data, **kwargs):
        self.io_data = io_data
        self.write_attributes(self.filename)
        self.write_ndarrays(self.filename)
        # no support for write_objects in CSV format


class CSVReader:
    @staticmethod
    def read_attributes(filename):
        with open(filename, mode='r') as meta_file:
            file_reader = csv.reader(meta_file, delimiter=',')
            meta_keys = file_reader.__next__()
            meta_values = file_reader.__next__()
        return dict(zip(meta_keys, meta_values))

    def process_metadict(self, meta_dict):
        attributes = {
            attr_name: utils.to_expression_or_string(attr_value) for attr_name, attr_value in meta_dict.items()
            if not re.match(r'dataset\d+', attr_name)
        }
        data_names = [dataname for datalabel, dataname in meta_dict.items() if re.match(r'dataset\d+$', datalabel)]
        data_slices = [ast.literal_eval(value) for key, value in meta_dict.items()
                       if re.match(r'dataset\d+.slices', key)]
        return attributes, data_names, data_slices

    @staticmethod
    def read_data(filename, slices):
        try:
            data_array = np.loadtxt(filename)
        except ValueError:
            data_array = np.loadtxt(filename, dtype=np.complex_)
        if slices > 1:
            nrows, ncols = data_array.shape
            return data_array.reshape((slices, nrows//slices, ncols))
        return data_array

    def from_file(self, filename, **kwargs):
        """
        Parameters
        ----------
        filename: str

        Returns
        -------
        class instance generated from file data
        """
        ext_attributes = self.read_attributes(filename)
        typename = ext_attributes['__type']
        del ext_attributes['__type']

        attributes, data_names, data_slices = self.process_metadict(ext_attributes)

        filename_stub, _ = os.path.splitext(filename)
        ndarrays = {}
        for index, dataname in enumerate(data_names):
            data_filename = filename_stub + '_' + dataname + '.csv'
            slices = data_slices[index]
            ndarrays[dataname] = self.read_data(data_filename, slices)

        return io.IOData(typename, attributes, ndarrays, objects=None)


def np_savetxt_3d(array3d, filename):
    """
    Helper function that splits a 3d numpy array into 2d slices for writing as csv data to a new file. Slices are
    separated by a comment row `# New slice`.

    Parameters
    ----------
    array3d: ndarray with ndim = 3
    filename: str
    """
    with open(filename, mode='w', newline='') as datafile:
        datafile.write('# Array shape: {0}\n'.format(array3d.shape))
        for data_slice in array3d:
            np.savetxt(datafile, data_slice)
            datafile.write('# New slice\n')

# fileio_backends.py # # This file is part of scqubits. # #    Copyright (c) 2019, <NAME> and <NAME> #    All rights reserved. # #    This source code is licensed under the BSD-style license found in the #    LICENSE file in the root directory of this source tree. ############################################################################ Helper routines for writing data to h5 files. ABC for writing class instance data to file.      Parameters     ----------     filename: str     file_handle: h5.Group, optional Writes IOData to a custom-format h5 file Attribute data consists of           1. `__init__` parameters that are of type str or numerical. These are directly written into `h5py.Group.attrs`          2. lists are stored under `<h5py.Group>/__lists`          3. dicts are stored under `<h5py.Group>/__dicts`          Parameters         ----------         h5file_group: h5py.Group # Record the type of the current class instance # h5py does not serialize dicts automatically, so have to do it manually Writes ndarray (float or complex) data contained in `self.iodata` to the provided `h5py.Group` as a         `h5py.Dataset`, using gzip compression.          Parameters         ----------         h5file_group: h5py.Group Writes data representing a Python object other than ndarray, list and dict, contained in `self.iodata` to the         provided `h5py.Group`  und `<h5py.Group>/__objects`.          Parameters         ----------         h5file_group: h5py.Group Takes the serialized IOData and writes it either to a new h5 file with file name given by `self.filename` to to         the given h5py.Group of an open h5 file.          Parameters         ----------         io_data: IOData         file_handle: h5py.Group, optional Enables reading h5 files generated with scqubits.      Parameters     ----------     filename: str     file_handle: h5py.Group, optional Converts h5 attribute data to a Python dictionary.          Parameters         ----------         h5_attrs: h5py.AttributeManager             as obtained by accessing `<h5py.Group>.attrs`          Returns         -------         dict [str, str or Number] Read data from h5 file group that is stored directly as `<h5py.Group>.attrs`, or saved in subgroups titled         `<h5py.Group>/__lists` and `<h5py.Group>/__dicts`.          Parameters         ----------         h5file_group: h5py.Group          Returns         -------         dict [str, dict or list] Read numpy array data from h5 file group.          Parameters         ----------         h5file_group: h5py.Group          Returns         -------         dict [str, ndarray] Read data from the given h5 file group that represents a Python object other than an ndarray, list, or dict.          Parameters         ----------         h5file_group: h5py.Group          Returns         -------         dict [str, IOData] Either opens a new h5 file for reading or accesses an already opened file via the given h5.Group handle. Reads         all data from the three categories of attributes (incl. lists and dicts), ndarrays, and objects.          Parameters         ----------         filename: str         file_handle: h5.Group, optional          Returns         -------         IOData Given filename='somename.csv', write initdata into somename.csv     Then, additional csv files are written for each dataset, with filenames: 'somename_' + dataname0 + '.csv' etc. Add data set information to attributes, so that dataset names and dimensions are available         in attributes CSV file. # no support for write_objects in CSV format Parameters         ----------         filename: str          Returns         -------         class instance generated from file data Helper function that splits a 3d numpy array into 2d slices for writing as csv data to a new file. Slices are     separated by a comment row `# New slice`.      Parameters     ----------     array3d: ndarray with ndim = 3     filename: str