import numpy as np
import os
import pandas as pd
import matplotlib.pyplot as plt
from ftfy import fix_text
import re
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
from scipy.sparse import csr_matrix
import sparse_dot_topn.sparse_dot_topn as ct



def ngrams(string, n=5):
    string = fix_text(string) # fix text encoding issues
    string = string.encode("ascii", errors="ignore").decode() #remove non ascii chars
    string = string.lower() #make lower case
    chars_to_remove = [")","(",".","|","[","]","{","}","'"]
    rx = '[' + re.escape(''.join(chars_to_remove)) + ']'
    string = re.sub(rx, '', string) #remove the list of chars defined above
    string = string.replace('&', 'and')
    string = string.replace(',', ' ')
    string = string.replace('-', ' ')
    string = string.title() # normalise case - capital at start of each word
    string = re.sub(' +',' ',string).strip() # get rid of multiple spaces and replace with a single space
    string = ' '+ string +' ' # pad names for ngrams...
    string = re.sub(r'[,-./]|\sBD',r'', string)
    ngrams = zip(*[string[i:] for i in range(n)])
    return [''.join(ngram) for ngram in ngrams]




def awesome_cossim_top(A, B, ntop, lower_bound=0):
    # force A and B as a CSR matrix.
    # If they have already been CSR, there is no overhead
    A = A.tocsr()
    B = B.tocsr()
    M, _ = A.shape
    _, N = B.shape
 
    idx_dtype = np.int32
 
    nnz_max = M*ntop
 
    indptr = np.zeros(M+1, dtype=idx_dtype)
    indices = np.zeros(nnz_max, dtype=idx_dtype)
    data = np.zeros(nnz_max, dtype=A.dtype)
    ct.sparse_dot_topn(
        M, N, np.asarray(A.indptr, dtype=idx_dtype),
        np.asarray(A.indices, dtype=idx_dtype),
        A.data,
        np.asarray(B.indptr, dtype=idx_dtype),
        np.asarray(B.indices, dtype=idx_dtype),
        B.data,
        ntop,
        lower_bound,
        indptr, indices, data)
    return csr_matrix((data,indices,indptr),shape=(M,N))

def get_matches_df(sparse_matrix, name_vector, top=840):
    non_zeros = sparse_matrix.nonzero()
    
    sparserows = non_zeros[0]
    sparsecols = non_zeros[1]
    
    if top:
        nr_matches = top
    else:
        nr_matches = sparsecols.size
    
    left_side = np.empty([nr_matches], dtype=object)
    right_side = np.empty([nr_matches], dtype=object)
    similairity = np.zeros(nr_matches)
    
    for index in range(0, nr_matches):
        left_side[index] = name_vector[sparserows[index]]
        right_side[index] = name_vector[sparsecols[index]]
        similairity[index] = sparse_matrix.data[index]
    
    return pd.DataFrame({'left_side': left_side,
                          'right_side': right_side,
                           'similarity': similairity})


