# coding: utf-8

"""
    Tickcatcher API

    The Tickcatcher API provides access to cryptocurrency market data including candlesticks, symbols, and indicators. All requests require authentication using your RapidAPI key. 

    The version of the OpenAPI document: 1.1.2
    Contact: yagiz@iskirik.com
    Generated by OpenAPI Generator (https://openapi-generator.tech)

    Do not edit the class manually.
"""  # noqa: E501


from __future__ import annotations
import pprint
import re  # noqa: F401
import json

from datetime import datetime
from pydantic import BaseModel, ConfigDict, Field, StrictInt, StrictStr
from typing import Any, ClassVar, Dict, List
from typing import Optional, Set
from typing_extensions import Self

class Metadata(BaseModel):
    """
    Metadata
    """ # noqa: E501
    symbol: StrictStr = Field(description="The trading pair symbol.")
    timeframe: StrictStr = Field(description="The timeframe used for the candles.")
    first_candle: datetime = Field(description="ISO timestamp of the first available candle.", alias="firstCandle")
    last_candle: datetime = Field(description="ISO timestamp of the last available candle.", alias="lastCandle")
    total_candles: StrictInt = Field(description="Total number of available candles.", alias="totalCandles")
    completion_percent: StrictStr = Field(description="Completion percentage (0–100) as a string with two decimals.", alias="completionPercent")
    estimated_completion_date: datetime = Field(description="Estimated date when full data collection will complete.", alias="estimatedCompletionDate")
    __properties: ClassVar[List[str]] = ["symbol", "timeframe", "firstCandle", "lastCandle", "totalCandles", "completionPercent", "estimatedCompletionDate"]

    model_config = ConfigDict(
        populate_by_name=True,
        validate_assignment=True,
        protected_namespaces=(),
    )


    def to_str(self) -> str:
        """Returns the string representation of the model using alias"""
        return pprint.pformat(self.model_dump(by_alias=True))

    def to_json(self) -> str:
        """Returns the JSON representation of the model using alias"""
        # TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
        return json.dumps(self.to_dict())

    @classmethod
    def from_json(cls, json_str: str) -> Optional[Self]:
        """Create an instance of Metadata from a JSON string"""
        return cls.from_dict(json.loads(json_str))

    def to_dict(self) -> Dict[str, Any]:
        """Return the dictionary representation of the model using alias.

        This has the following differences from calling pydantic's
        `self.model_dump(by_alias=True)`:

        * `None` is only added to the output dict for nullable fields that
          were set at model initialization. Other fields with value `None`
          are ignored.
        """
        excluded_fields: Set[str] = set([
        ])

        _dict = self.model_dump(
            by_alias=True,
            exclude=excluded_fields,
            exclude_none=True,
        )
        return _dict

    @classmethod
    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
        """Create an instance of Metadata from a dict"""
        if obj is None:
            return None

        if not isinstance(obj, dict):
            return cls.model_validate(obj)

        _obj = cls.model_validate({
            "symbol": obj.get("symbol"),
            "timeframe": obj.get("timeframe"),
            "firstCandle": obj.get("firstCandle"),
            "lastCandle": obj.get("lastCandle"),
            "totalCandles": obj.get("totalCandles"),
            "completionPercent": obj.get("completionPercent"),
            "estimatedCompletionDate": obj.get("estimatedCompletionDate")
        })
        return _obj


