Module hdict.text.customjson

#  Copyright (c) 2023. Davi Pereira dos Santos
#  This file is part of the hdict project.
#  Please respect the license - more about this in the section (*) below.
#
#  hdict is free software: you can redistribute it and/or modify
#  it under the terms of the GNU General Public License as published by
#  the Free Software Foundation, either version 3 of the License, or
#  (at your option) any later version.
#
#  hdict is distributed in the hope that it will be useful,
#  but WITHOUT ANY WARRANTY; without even the implied warranty of
#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#  GNU General Public License for more details.
#
#  You should have received a copy of the GNU General Public License
#  along with hdict.  If not, see <http://www.gnu.org/licenses/>.
#
#  (*) Removing authorship by any means, e.g. by distribution of derived
#  works or verbatim, obfuscated, compiled or rewritten versions of any
#  part of this work is illegal and it is unethical regarding the effort and
#  time spent here.
#
import json
import re
from json import JSONEncoder

from hdict.content.entry import AbsEntry


class CustomJSONEncoder(JSONEncoder):
    """
    >>> from hdict import hdict
    >>> a = hdict(x=3)
    >>> hdict(d=a, y=5).show(colored=False)
    {
        d: {
            x: 3,
            _id: fBb9FHVYpHC7vyM-B8UrXuN4oCcQ4Y7pnQ6oSK3J,
            _ids: {
                x: KGWjj0iyLAn1RG6RTGtsGE3omZraJM6xO.kvG5pr
            }
        },
        y: 5,
        _id: YyAtuyiPhC7pHV-ADAoh1Lp30TM-08swr40vOmk1,
        _ids: {
            d: fBb9FHVYpHC7vyM-B8UrXuN4oCcQ4Y7pnQ6oSK3J,
            y: ecvgo-CBPi7wRWIxNzuo1HgHQCbdvR058xi6zmr2
        }
    }
    >>> from pandas.core.frame import DataFrame, Series
    >>> df = DataFrame([[1,2],[3,4]])
    >>> df
       0  1
    0  1  2
    1  3  4
    >>> b = hdict(d=a, y=5, df_=df, ell=...)
    >>> b.show(colored=False)
    {
        d: {
            x: 3,
            _id: fBb9FHVYpHC7vyM-B8UrXuN4oCcQ4Y7pnQ6oSK3J,
            _ids: {
                x: KGWjj0iyLAn1RG6RTGtsGE3omZraJM6xO.kvG5pr
            }
        },
        y: 5,
        df_: "‹{0: {0: 1, 1: 3}, 1: {0: 2, 1: 4}}›",
        ell: "...",
        df: {
            index: "‹{0: 0, 1: 1}›",
            0: "‹{0: 1, 1: 3}›",
            1: "‹{0: 2, 1: 4}›",
            _id: 5AEwwOH-Pce.9hYv7iKRH4Y3fb0Sn87Y9hjc2KSq,
            _ids: {
                index: QdEJIrQysoxHLAyadXmhHZEOJ-uUQHDU2I8c6Sa5,
                0: g21M8pVG-91m4ekI5WLbhsilHR5yY6x41SGUhJNk,
                1: jT2rJg-ZO6jg9V5UJnXpPTZM9qZ1cUYYMqMfdj5j
            }
        },
        _id: k6uCZEvebQoeSVoQjrgYJJ3eGAoN5culz7tRBRzU,
        _ids: {
            d: fBb9FHVYpHC7vyM-B8UrXuN4oCcQ4Y7pnQ6oSK3J,
            y: ecvgo-CBPi7wRWIxNzuo1HgHQCbdvR058xi6zmr2,
            ell: P1oPe-8hTjTdV6gKov4oIQnmTUXyD2fU6E7C8MS6,
            df: 5AEwwOH-Pce.9hYv7iKRH4Y3fb0Sn87Y9hjc2KSq,
            df_: dDWRsi5u5lmXCdQ8w0OnCFclDcWJtuLQCmoLYJrz
        }
    }
    >>> from numpy import array
    >>> hdict(b=b, z=9, c=(c:=array([1,2,3])), d=Series(c), dd=array([[1, 2], [3, 4]])).show(colored=False)
    {
        b: {
            d: {
                x: 3,
                _id: fBb9FHVYpHC7vyM-B8UrXuN4oCcQ4Y7pnQ6oSK3J,
                _ids: {
                    x: KGWjj0iyLAn1RG6RTGtsGE3omZraJM6xO.kvG5pr
                }
            },
            y: 5,
            df_: "‹{0: {0: 1, 1: 3}, 1: {0: 2, 1: 4}}›",
            ell: "...",
            df: {
                index: "‹{0: 0, 1: 1}›",
                0: "‹{0: 1, 1: 3}›",
                1: "‹{0: 2, 1: 4}›",
                _id: 5AEwwOH-Pce.9hYv7iKRH4Y3fb0Sn87Y9hjc2KSq,
                _ids: {
                    index: QdEJIrQysoxHLAyadXmhHZEOJ-uUQHDU2I8c6Sa5,
                    0: g21M8pVG-91m4ekI5WLbhsilHR5yY6x41SGUhJNk,
                    1: jT2rJg-ZO6jg9V5UJnXpPTZM9qZ1cUYYMqMfdj5j
                }
            },
            _id: k6uCZEvebQoeSVoQjrgYJJ3eGAoN5culz7tRBRzU,
            _ids: {
                d: fBb9FHVYpHC7vyM-B8UrXuN4oCcQ4Y7pnQ6oSK3J,
                y: ecvgo-CBPi7wRWIxNzuo1HgHQCbdvR058xi6zmr2,
                ell: P1oPe-8hTjTdV6gKov4oIQnmTUXyD2fU6E7C8MS6,
                df: 5AEwwOH-Pce.9hYv7iKRH4Y3fb0Sn87Y9hjc2KSq,
                df_: dDWRsi5u5lmXCdQ8w0OnCFclDcWJtuLQCmoLYJrz
            }
        },
        z: 9,
        c: "‹[1 2 3]›",
        d: "‹{0: 1, 1: 2, 2: 3}›",
        dd: "‹[[1 2] [3 4]]›",
        _id: ohw1N1eWttnXESOq2fY1LZ5ysZDwr786kqQrWDMw,
        _ids: {
            b: k6uCZEvebQoeSVoQjrgYJJ3eGAoN5culz7tRBRzU,
            z: GuwIQCrendfKXZr5jGfrUwoP-8TWMhmLHYrja2yj,
            c: QkfVsy7ITAmoIiOFgbYpsQodBSIYshhiUm3v2r8d,
            d: GxcSmtUACjP6u10QYPRJsa6OyoAwK96RBma9qt7o,
            dd: fVj30baMeet4PcN9ZY-8uMpFin89FY8h8MI4RkDd
        }
    }
    >>> hdict(s={1,3,2}, t={3,1,2}, u={2,1,3}, v={2,1,3}).show(colored=False)
    {
        s: "{1, 2, 3}",
        t: "{1, 2, 3}",
        u: "{1, 2, 3}",
        v: "{1, 2, 3}",
        _id: 0WaiiSdaTRaHqFjzD-Y8Mm2v1Ym.Nj9UUxPIVMax,
        _ids: {
            s: kDsYXHYuZJ9o7GXZLx6lYu0GpEqyrCmBpt0xoy60,
            t: kDsYXHYuZJ9o7GXZLx6lYu0GpEqyrCmBpt0xoy60,
            u: kDsYXHYuZJ9o7GXZLx6lYu0GpEqyrCmBpt0xoy60,
            v: kDsYXHYuZJ9o7GXZLx6lYu0GpEqyrCmBpt0xoy60
        }
    }
    >>> str(hdict(s={1,3,2}, t={3,1,2}, u={2,1,3}, v={2,1,3}))
    '{s: "{1, 2, 3}", t: "{1, 2, 3}", u: "{1, 2, 3}", v: "{1, 2, 3}"}'
    """

    width = 200

    def default(self, obj):
        if obj is not None:
            # from hoshmap import FrozenIdict, Idict
            # if isinstance(obj, Idict):
            #     return obj.frozen.asdicts
            # if isinstance(obj, FrozenIdict):
            #     return obj.asdicts
            if obj is Ellipsis:
                return "..."
            if isinstance(obj, AbsEntry) and obj.isevaluated:
                from hdict import hdict, frozenhdict

                if isinstance(obj.value, (frozenhdict, hdict)):
                    return obj.value.asdicts_noid
                return obj.value
            # if isinstance(obj, FunctionType):
            #     return str(obj)
            if not isinstance(obj, (dict, list, str, int, float, bytearray, bool)):
                if type(obj).__name__ in ["DataFrame", "Series"]:
                    # ‹str()› is to avoid nested identation
                    return f"‹{truncate(str(obj.to_dict()), self.width)}›"
                if type(obj).__name__ == "ndarray":
                    txt = str(obj).replace("\n", "")
                    return f"‹{truncate(txt, self.width)}›"
                return truncate(str(obj).replace("\n", ""), self.width)
        return JSONEncoder.default(self, obj)  # pragma: no cover


# class CustomJSONDecoder(JSONDecoder):
#     def __init__(self, *args, **kwargs):
#         JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
#
#     def object_hook(self, obj):
#         if obj is not None:
#             if isinstance(obj, str) and len(obj) == digits:
#                 return
#         return obj


def truncate(txt, width=200):
    """
    >>> truncate("lkjsdflkjsdf", width=2)
    'lk···'
    """
    if len(txt) > width:
        txt = txt[:width] + ("···›" if txt.endswith("›") else ("···)" if txt.endswith(")") else "···"))
    return txt


def stringfy(obj):
    res = json.dumps(obj, ensure_ascii=False, cls=CustomJSONEncoder)
    return re.sub(r'(?<!: )"(\S*?)"', "\\1", res)