from flask import Flask, jsonify, request
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from flask_cors import CORS
from collections import defaultdict, Counter
from time import time
import dask.dataframe as dd
import os.path
import json
from sklearn import preprocessing
from functools import partial
from itertools import groupby
from multiprocessing import Pool

app = Flask(__name__)
CORS(app)

@app.route('/', methods=['GET'])
def index():
    return "hi"

@app.route('/read-data', methods=['GET'])
def read_data():
    filename = 'processed-data.pkl'
    if (not os.path.isfile(filename)):
        print("start")
        df = dd.read_csv("NW_Ground_Stations_2016.csv", usecols=['number_sta', 'date', 't'])
        print("read file")
        df = df.loc[df['number_sta'] == 14066001]
        print("split rows")
        df = df.compute()
        df.to_pickle(filename)
        print("to_pandas")
    df = pd.read_pickle(filename)
    df.dropna(subset=['t'], inplace=True)
    response = {
        "index": json.dumps(df.loc[:, 'date'].values.astype(str).tolist()),
        "values": json.dumps(df.loc[:, 't'].values.astype(str).tolist())
    }
    print("response ready")
    response = jsonify(response)
    return response

# @app.route('/read-data', methods=['GET'])
# def read_data():
#     df = pd.read_csv("1.csv", index_col=3)
#     df.index = pd.to_datetime(df.index)
#     df.sort_index(inplace=True)
#     meantemp = df.loc[:, 7].copy()
#     response = {
#         "index": meantemp.index.values.astype(str).tolist(),
#         "values": meantemp.values.tolist()
#     }
#     response = jsonify(response)
#     return response

@app.route('/create-windows', methods=['POST'])
def create_windows():
    raw_data = request.json
    values = raw_data["values"]
    window_size = int(raw_data['parameters']["windowsize"])
    data = [values[i:i+window_size] for i in range(len(values) - window_size)]
    data = preprocessing.minmax_scale(data, (-1, 1), axis=1)
    response = jsonify(data.tolist())
    return response

def fill_table(data, hash_functions, index):
    table = defaultdict(list)
    signatures = [''.join((np.dot(data[window_index], hash_functions[index]) > 0).astype('int').astype('str')) for window_index in
                  range(data.shape[0])]
    counted_sig = enumerate(signatures)
    for i, x in counted_sig:
        table[x].append(i)
    return table

@app.route('/create-tables', methods=['POST'])
def create_tables():
    t0 = time()
    raw_data = request.json
    data = raw_data["windows"]
    window_size = int(raw_data['parameters']["windowsize"])
    hash_size = int(raw_data['parameters']["hashsize"])
    table_size = int(raw_data['parameters']["tablesize"])
    data = np.array(data)
    tables_hash_function = [np.random.uniform(-1, 1, size=(window_size, hash_size)) for _ in range(table_size)]
    print('Init time: ' + str(time() - t0))

    try:
        pool = Pool()
        func = partial(fill_table, data, tables_hash_function)
        print('Starting pool: ' + str(time() - t0))
        tables = pool.map(func, range(table_size))
    finally:
        pool.close()
        pool.join()

    print('Creation time: ' + str(time() - t0))
    hash_functions = np.array(tables_hash_function).tolist()
    response = {}
    for table_index in range(table_size):
        response[table_index] = {
            "hash": hash_functions[table_index],
            "entries": tables[table_index]
        }
    response = jsonify(response)
    return response

@app.route('/query', methods=['POST'])
def query():
    raw_data = request.json
    window = raw_data["window"]
    tables = raw_data["tables"]
    neighbours = []

    output = {}

    for t in tables.values():
        signature = ''.join((np.dot(window, t["hash"]) > 0).astype('int').astype('str'))
        neighbours.extend(t["entries"][signature])
    neighbours_with_frequency = dict(Counter(neighbours))
    for index, frequency in neighbours_with_frequency.items():
        if not frequency in output:
            output[frequency] = []
        output[frequency].append(index)
    response = jsonify(output)
    return response

def create_valid_table(data, window_size, hash_size, correct_indices, incorrect_indices, index):
    entries = defaultdict(list)
    while True:
        hash_function = np.random.randn(window_size, hash_size)
        correct_signatures = [''.join((np.dot(data[index], hash_function) > 0).astype('int').astype('str')) for index in
                              correct_indices]
        incorrect_signatures = [''.join((np.dot(data[index], hash_function) > 0).astype('int').astype('str')) for index
                                in incorrect_indices]
        if correct_signatures.count(correct_signatures[0]) == len(correct_signatures) and incorrect_signatures.count(
                correct_signatures[0]) == 0:
            break
    for window_index in range(data.shape[0]):
        signature = ''.join((np.dot(data[window_index], hash_function) > 0).astype('int').astype('str'))
        entries[signature].append(window_index)
    return {
        "hash": hash_function.tolist(),
        "entries": entries
    }

@app.route('/update', methods=['POST'])
def update():
    t0 = time()
    raw_data = request.json

    data = raw_data["windows"]
    data = np.array(data)
    label_data = raw_data["labelData"]
    tables = raw_data["tables"]

    window_size = int(raw_data['parameters']["windowsize"])
    hash_size = int(raw_data['parameters']["hashsize"])
    table_size = int(raw_data['parameters']["tablesize"])
    new_tables = []

    correct_indices = [int(index) for index, value in label_data.items() if value is True]
    incorrect_indices = [int(index) for index, value in label_data.items() if value is False]

    window = data[correct_indices[0]]

    for t in tables.values():
        valid = True
        signature = ''.join((np.dot(window, t["hash"]) > 0).astype('int').astype('str'))
        neighbours = t["entries"][signature]
        for index in correct_indices:
            if index not in neighbours:
                valid = False
                break
        for index in incorrect_indices:
            if index in neighbours:
                valid = False
                break
        if valid:
            new_tables.append(t)

    try:
        pool = Pool()
        func = partial(create_valid_table, data, window_size, hash_size, correct_indices, incorrect_indices)
        print('Starting pool: ' + str(time() - t0))
        new_tables.extend(pool.map(func, range(table_size - len(new_tables))))
    finally:
        pool.close()
        pool.join()

    print('Update time: ' + str(time() - t0))
    response = {}
    for table_index in range(len(new_tables)):
        response[table_index] = {
            "hash": new_tables[table_index]["hash"],
            "entries": new_tables[table_index]["entries"]
        }
    response = jsonify(response)
    return response