import numpy as np
from numpy.typing import ArrayLike
import uproot as ur
from typing import Any, Iterable
from .detectors import PXD
from .common import FancyDict
try:
    import pandas as pd
    _pandas = True
except ImportError:
    _pandas = False


class Rootable:
    """
    this class uses uproot to load pxd data from root files and converts them into
    native python data structures.
    it can load the cluster information, uses the digits to generate the adc matrices,
    coordinates, layer and ladders and finally also monte carlo data.
    """
    def __init__(self, data: dict = None) -> None:
        self.pxd = PXD()

        # indices for events to be imported
        self.eventIndices = None

        # the root event tree
        self.eventTree = None

        # import flags
        self.gotClusters = False
        self.gotDigits = False
        self.gotMatrices = False
        self.gotCoordinates = False
        self.gotMCData = False
        self.gotFiltered = False

    def where(self, *conditions: str) -> dict:
        return self.pxd.where(*conditions)

    @property
    def data(self) -> dict:
        return {'pxd': self.pxd.data}

    def __repr__(self) -> str:
        return str(self.pxd)

    def __iter__(self) -> Iterable:
        return iter(self.pxd.data)

    def __len__(self) -> int:
        return len(self.pxd.data)

    def __getitem__(self, index: str | int | ArrayLike) -> FancyDict:
        """
        this makes the class subscriptable, one can retrieve one coloumn by using
        strings as keywords, or get a row by using integer indices or arrays
        """
        if isinstance(index, str):
            return self.data['pxd'][index]
        return FancyDict({key: value[index] for key, value in self.data['pxd'].items()})

    @property
    def numEvents(self) -> int:
        return len(np.unique(self.pxd['eventNumber']))

    @property
    def numClusters(self) -> int:
        return len(self.pxd['clsCharge'])

    @property
    def particles(self) -> list:
        return np.unique(self.pxd['pdg'])

    def keys(self) -> dict:
        return {'pxd': self.pxd.keys()}

    def items(self) -> dict:
        return {'pxd': self.pxd.items()}

    def values(self) -> dict:
        return {'pxd': self.pxd.values()}

    def get(self, key: str) -> np.ndarray:
        return self.pxd.get(key)

    def pop(self, key: str) -> None:
        return self.pxd.pop(key)

    def stack(self, *columns, toKey: str, pop: bool = True) -> None:
       self.pxd.stack(*columns, toKey=toKey)

    def loadData(self, fileName: str, includeUnSelected: bool = False) -> None:
        """
        Reads the file off of the hard drive; it automatically creates event numbers.
        file: str = it's the whole file path + .root ending
        """
        file, _, treeName = fileName.partition(':')
        self.includeUnSelected = includeUnSelected

        if not file.endswith('.root'):
            file += '.root'

        if not treeName:
            treeName = 'tree'

        try: # checking if file exists
            with open(file, 'r') as f:
                self.eventTree = ur.open(f'{file}:{treeName}')
        except FileNotFoundError:
            raise FileNotFoundError(f"File {file} not found.")

    def getClusters(self) -> None:
        self.gotClusters = True
        self.pxd.getClusters(self.eventTree, self.includeUnSelected)

    def getDigits(self) -> None:
        self.gotDigits = True
        self.pxd.getDigits(self.eventTree, self.includeUnSelected)

    def getMatrices(self, matrixSize: tuple = (9, 9)) -> None:
        self.gotMatrices = True
        self.pxd.getMatrices(self.eventTree, matrixSize=matrixSize)

    def getCoordinates(self) -> None:
        self.gotCoordinates = True
        self.pxd.getCoordinates(self.eventTree)

    def getSphericals(self) -> None:
        self.pxd.getSphericals()

    def getLayers(self) -> None:
        self.pxd.getLayers(self.eventTree)

    def getMCData(self) -> None:
        self.gotMCData = True
        self.pxd.getMCData(self.eventTree)

    def getFiltered(self) -> None:
        self.gotFiltered = True
        self.pxd.getFiltered(self.eventTree)

    def asStructuredArray(self) -> np.ndarray:
        """
        this converts the data dict of this class into a structured numpy array
        """
        # Create a list to hold the dtype specifications
        dtype = []

        # Iterate through the dictionary keys and values
        for key, value in self.pxd.items():
            # Determine the data type of the first value in the list
            sampleValue = value[0]

            if isinstance(sampleValue, np.ndarray):
                # If the value is an array, use its shape and dtype
                shapes = [val.shape for val in value]
                if not all(shape == shapes[0] for shape in shapes):
                       fieldDtype = object
                else:
                    fieldDtype = (sampleValue.dtype, sampleValue.shape)
            else:
                # Otherwise, use the type of the value itself
                fieldDtype = type(sampleValue)

            # Append the key and data type to the dtype list
            dtype.append((key, fieldDtype))

        # Convert the dictionary to a list of tuples
        keys = list(self.pxd.keys())
        dataList = [tuple(self.pxd[key][i] for key in keys) for i in range(len(self.pxd[keys[0]]))]

        # Create the structured array
        structuredArray = np.array(dataList, dtype=dtype)

        return structuredArray

    def asDict(self) -> dict:
        return {'pxd': self.pxd.data}

    def asDataFrame(self, popMatrices: bool = False):
        if _pandas:
            if self.gotMatrices and not popMatrices:
                raise TypeError('pandas does not handle 2D matrices')
            if popMatrices:
                self.pxd.pop('cluster')
            return pd.DataFrame(self.pxd.data)
        else:
            raise ImportError('pandas is not installed on this system')