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#!/usr/bin/env python 
"""
Retrieve intraday stock data from Google Finance.
"""

import csv
import datetime
import re

import pandas as pd

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def append_variable_to_url(URL, var_dict):
    '''
    Properly format string to append variable to URL
    :param var_dict:
    :return: URL variables string
    '''
    var_string = ""

    for key in var_dict:
        if var_string == "":

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%load_ext autoreload
%autoreload 2
%matplotlib inline 

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def Find_Optimal_Cutoff(target, predicted):
    """ Find the optimal probability cutoff point for a classification model related to event rate
    Parameters
    ----------
    target : Matrix with dependent or target data, where rows are observations

    predicted : Matrix with predicted data, where rows are observations

    Returns
    -------     

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from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.validation import check_X_y, check_is_fitted
from sklearn.linear_model import LogisticRegression
from scipy import sparse
class NbSvmClassifier(BaseEstimator, ClassifierMixin):
    def __init__(self, C=1.0, dual=False, n_jobs=1):
        self.C = C
        self.dual = dual
        self.n_jobs = n_jobs

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class Attention(Layer):
    def __init__(self, step_dim,
                 W_regularizer=None, b_regularizer=None,
                 W_constraint=None, b_constraint=None,
                 bias=True, **kwargs):
        """
        Keras Layer that implements an Attention mechanism for temporal data.
        Supports Masking.
        Follows the work of Raffel et al. [https://arxiv.org/abs/1512.08756]
        # Input shape

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import os
import hashlib


def generate_cache_recursive(path, cache):
    '''Recursivly generate the cache of files in path. SHA256 is used.

    path - string that is the path os a folder
    cache - set that is the cache
    

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du -a | cut -d/ -f2 | sort | uniq -c | sort -nr