import re
import copy

import pandas as pd
import numpy as np

from . import util

class Table:
    """Store spreadsheet-like data."""

    def __init__(self,data,shapes,comments=None):
        """
        New spreadsheet.

        Parameters
        ----------
        data : numpy.ndarray or pandas.DataFrame
            Data. Column labels from a pandas.DataFrame will be replaced.
        shapes : dict with str:tuple pairs
            Shapes of the columns. Example 'F':(3,3) for a deformation gradient.
        comments : str or iterable of str, optional
            Additional, human-readable information.

        """
        comments_ = [comments] if isinstance(comments,str) else comments
        self.comments = [] if comments_ is None else [c for c in comments_]
        self.data = pd.DataFrame(data=data)
        self.shapes = { k:(v,) if isinstance(v,(np.int,int)) else v for k,v in shapes.items() }
        self._label_uniform()


    def __copy__(self):
        """Copy Table."""
        return copy.deepcopy(self)

    def copy(self):
        """Copy Table."""
        return self.__copy__()


    def _label_discrete(self):
        """Label data individually, e.g. v v v ==> 1_v 2_v 3_v."""
        labels = []
        for label,shape in self.shapes.items():
            size = int(np.prod(shape))
            labels += [('' if size == 1 else f'{i+1}_')+label for i in range(size)]
        self.data.columns = labels


    def _label_uniform(self):
        """Label data uniformly, e.g. 1_v 2_v 3_v ==> v v v."""
        labels = []
        for label,shape in self.shapes.items():
            labels += [label] * int(np.prod(shape))
        self.data.columns = labels


    def _add_comment(self,label,shape,info):
        if info is not None:
            specific = f'{label}{" "+str(shape) if np.prod(shape,dtype=int) > 1 else ""}: {info}'
            general  = util.execution_stamp('Table')
            self.comments.append(f'{specific} / {general}')


    @staticmethod
    def load_ASCII(fname):
        """
        Load ASCII table file.

        In legacy style, the first line indicates the number of
        subsequent header lines as "N header", with the last header line being
        interpreted as column labels.
        Alternatively, initial comments are marked by '#', with the first non-comment line
        containing the column labels.
        Vector data column labels are indicated by '1_v, 2_v, ..., n_v'.
        Tensor data column labels are indicated by '3x3:1_T, 3x3:2_T, ..., 3x3:9_T'.

        Parameters
        ----------
        fname : file, str, or pathlib.Path
            Filename or file for reading.

        """
        try:
            f = open(fname)
        except TypeError:
            f = fname
            f.seek(0)

        try:
            N_comment_lines,keyword = f.readline().strip().split(maxsplit=1)
            if keyword != 'header':
                raise ValueError
            else:
                comments = [f.readline().strip() for i in range(1,int(N_comment_lines))]
                labels   = f.readline().split()
        except ValueError:
            f.seek(0)
            comments = []
            line = f.readline().strip()
            while line.startswith('#'):
                comments.append(line.lstrip('#').strip())
                line = f.readline().strip()
            labels = line.split()

        shapes = {}
        for label in labels:
            tensor_column = re.search(r'[0-9,x]*?:[0-9]*?_',label)
            if tensor_column:
                my_shape = tensor_column.group().split(':',1)[0].split('x')
                shapes[label.split('_',1)[1]] = tuple([int(d) for d in my_shape])
            else:
                vector_column = re.match(r'[0-9]*?_',label)
                if vector_column:
                    shapes[label.split('_',1)[1]] = (int(label.split('_',1)[0]),)
                else:
                    shapes[label] = (1,)

        data = pd.read_csv(f,names=list(range(len(labels))),sep=r'\s+')

        return Table(data,shapes,comments)

    @staticmethod
    def load_ang(fname):
        """
        Load ang file.

        A valid TSL ang file needs to contains the following columns:
        * Euler angles (Bunge notation) in radians, 3 floats, label 'eu'.
        * Spatial position in meters, 2 floats, label 'pos'.
        * Image quality, 1 float, label 'IQ'.
        * Confidence index, 1 float, label 'CI'.
        * Phase ID, 1 int, label 'ID'.
        * SEM signal, 1 float, label 'intensity'.
        * Fit, 1 float, label 'fit'.

        Parameters
        ----------
        fname : file, str, or pathlib.Path
            Filename or file for reading.

        """
        try:
            f = open(fname)
        except TypeError:
            f = fname
            f.seek(0)

        content = f.readlines()

        comments = [util.execution_stamp('Table','from_ang')]
        for line in content:
            if line.startswith('#'):
                comments.append(line.strip())
            else:
                break

        data = np.loadtxt(content)

        shapes = {'eu':3, 'pos':2, 'IQ':1, 'CI':1, 'ID':1, 'intensity':1, 'fit':1}
        remainder = data.shape[1]-sum(shapes.values())
        if remainder > 0:                                                       # 3.8 can do: if (remainder := data.shape[1]-sum(shapes.values())) > 0
            shapes['unknown'] = remainder

        return Table(data,shapes,comments)


    @property
    def labels(self):
        return list(self.shapes.keys())


    def get(self,label):
        """
        Get column data.

        Parameters
        ----------
        label : str
            Column label.

        """
        if re.match(r'[0-9]*?_',label):
            idx,key = label.split('_',1)
            data = self.data[key].to_numpy()[:,int(idx)-1].reshape(-1,1)
        else:
            data = self.data[label].to_numpy().reshape((-1,)+self.shapes[label])

        return data.astype(type(data.flatten()[0]))


    def set(self,label,data,info=None):
        """
        Set column data.

        Parameters
        ----------
        label : str
            Column label.
        data : np.ndarray
            New data.
        info : str, optional
            Human-readable information about the new data.

        """
        dup = self.copy()
        dup._add_comment(label,data.shape[1:],info)

        if re.match(r'[0-9]*?_',label):
            idx,key = label.split('_',1)
            iloc = dup.data.columns.get_loc(key).tolist().index(True) + int(idx) -1
            dup.data.iloc[:,iloc] = data
        else:
            dup.data[label]       = data.reshape(dup.data[label].shape)
        return dup

    def add(self,label,data,info=None):
        """
        Add column data.

        Parameters
        ----------
        label : str
            Column label.
        data : np.ndarray
            Modified data.
        info : str, optional
            Human-readable information about the modified data.

        """
        dup = self.copy()
        dup._add_comment(label,data.shape[1:],info)

        dup.shapes[label] = data.shape[1:] if len(data.shape) > 1 else (1,)
        size = np.prod(data.shape[1:],dtype=int)
        new = pd.DataFrame(data=data.reshape(-1,size),
                           columns=[label]*size,
                          )
        new.index = dup.data.index
        dup.data = pd.concat([dup.data,new],axis=1)
        return dup


    def delete(self,label):
        """
        Delete column data.

        Parameters
        ----------
        label : str
            Column label.

        """
        dup = self.copy()
        dup.data.drop(columns=label,inplace=True)
        del dup.shapes[label]
        return dup


    def rename(self,old,new,info=None):
        """
        Rename column data.

        Parameters
        ----------
        label_old : str or iterable of str
            Old column label(s).
        label_new : str or iterable of str
            New column label(s).

        """
        dup = self.copy()
        columns = dict(zip([old] if isinstance(old,str) else old,
                           [new] if isinstance(new,str) else new))
        dup.data.rename(columns=columns,inplace=True)
        dup.comments.append(f'{old} => {new}'+('' if info is None else f': {info}'))
        dup.shapes = {(label if label not in columns else columns[label]):dup.shapes[label] for label in dup.shapes}
        return dup


    def sort_by(self,labels,ascending=True):
        """
        Sort table by values of given labels.

        Parameters
        ----------
        label : str or list
            Column labels for sorting.
        ascending : bool or list, optional
            Set sort order.

        """
        dup = self.copy()
        dup._label_discrete()
        dup.data.sort_values(labels,axis=0,inplace=True,ascending=ascending)
        dup._label_uniform()
        dup.comments.append(f'sorted {"ascending" if ascending else "descending"} by {labels}')
        return dup


    def append(self,other):
        """
        Append other table vertically (similar to numpy.vstack).

        Requires matching labels/shapes and order.

        Parameters
        ----------
        other : Table
            Table to append.

        """
        if self.shapes != other.shapes or not self.data.columns.equals(other.data.columns):
            raise KeyError('Labels or shapes or order do not match')
        else:
            dup = self.copy()
            dup.data = dup.data.append(other.data,ignore_index=True)
            return dup


    def join(self,other):
        """
        Append other table horizontally (similar to numpy.hstack).

        Requires matching number of rows and no common labels.

        Parameters
        ----------
        other : Table
            Table to join.

        """
        if set(self.shapes) & set(other.shapes) or self.data.shape[0] != other.data.shape[0]:
            raise KeyError('Dublicated keys or row count mismatch')
        else:
            dup = self.copy()
            dup.data = dup.data.join(other.data)
            for key in other.shapes:
                dup.shapes[key] = other.shapes[key]
            return dup


    def save_ASCII(self,fname,legacy=False):
        """
        Save as plain text file.

        Parameters
        ----------
        fname : file, str, or pathlib.Path
            Filename or file for writing.
        legacy : Boolean, optional
            Write table in legacy style, indicating header lines by "N header"
            in contrast to using comment sign ('#') at beginning of lines.

        """
        seen = set()
        labels = []
        for l in [x for x in self.data.columns if not (x in seen or seen.add(x))]:
            if self.shapes[l] == (1,):
                labels.append(f'{l}')
            elif len(self.shapes[l]) == 1:
                labels += [f'{i+1}_{l}' \
                          for i in range(self.shapes[l][0])]
            else:
                labels += [f'{util.srepr(self.shapes[l],"x")}:{i+1}_{l}' \
                          for i in range(np.prod(self.shapes[l]))]

        header = ([f'{len(self.comments)+1} header'] + self.comments) if legacy else \
                  [f'# {comment}' for comment in self.comments]

        try:
            fhandle = open(fname,'w')
        except TypeError:
            fhandle = fname

        for line in header + [' '.join(labels)]: fhandle.write(line+'\n')
        self.data.to_csv(fhandle,sep=' ',na_rep='nan',index=False,header=False)