wavelets/wave_lite.py at master · mnlx/wavelets · GitHub

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import pywt
#import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
#import scipy.stats as sci
import os


def upArrow_op(li, j):
    if j == 0:
        return [1]
    N = len(li)
    li_n = np.zeros(2 ** (j - 1) * (N - 1) + 1)
    for i in range(N):
        li_n[2 ** (j - 1) * i] = li[i]
    return li_n


def period_list(li, N):
    n = len(li)
    # append [0 0 ...]
    n_app = N - np.mod(n, N)
    li = list(li)
    li = li + [0] * n_app
    if len(li) < 2 * N:
        return np.array(li)
    else:
        li = np.array(li)
        li = np.reshape(li, [-1, N])
        li = np.sum(li, axis=0)
        return li


def circular_convolve_mra(h_j_o, w_j):
#    ''' calculate the mra D_j'''
    N = len(w_j)
    l = np.arange(N)
    D_j = np.zeros(N)
    for t in range(N):
        index = np.mod(t + l, N)
        w_j_p = np.array([w_j[ind] for ind in index])
        D_j[t] = (np.array(h_j_o) * w_j_p).sum()
    return D_j


def circular_convolve_d(h_t, v_j_1, j):
#    '''
#    jth level decomposition
#    h_t: \tilde{h} = h / sqrt(2)
#    v_j_1: v_{j-1}, the (j-1)th scale coefficients
#    return: w_j (or v_j)
#    '''
    N = len(v_j_1)
    L = len(h_t)
    w_j = np.zeros(N)
    l = np.arange(L)
    for t in range(N):
        index = np.mod(t - 2 ** (j - 1) * l, N)
        v_p = np.array([v_j_1[ind] for ind in index])
        w_j[t] = (np.array(h_t) * v_p).sum()
    return w_j


def circular_convolve_s(h_t, g_t, w_j, v_j, j):
#    '''
#    (j-1)th level synthesis from w_j, w_j
#    see function circular_convolve_d
#    '''
    N = len(v_j)
    L = len(h_t)
    v_j_1 = np.zeros(N)
    l = np.arange(L)
    for t in range(N):
        index = np.mod(t + 2 ** (j - 1) * l, N)
        w_p = np.array([w_j[ind] for ind in index])
        v_p = np.array([v_j[ind] for ind in index])
        v_j_1[t] = (np.array(h_t) * w_p).sum()
        v_j_1[t] = v_j_1[t] + (np.array(g_t) * v_p).sum()
    return v_j_1


def modwt(x, filters, level):
#    '''
#    filters: 'db1', 'db2', 'haar', ...
#    return: see matlab
#    '''
    # filter
    wavelet = pywt.Wavelet(filters)
    h = wavelet.dec_hi
    g = wavelet.dec_lo
    h_t = np.array(h) / np.sqrt(2)
    g_t = np.array(g) / np.sqrt(2)
    wavecoeff = []
    v_j_1 = x
#==============================================================================
#
#  Importante: j é o nível de frequência que se deseja
#
#
#==============================================================================
    for j in range(level):
        w = circular_convolve_d(h_t, v_j_1, j + 1)
        v_j_1 = circular_convolve_d(g_t, v_j_1, j + 1)
        wavecoeff.append(w)
    wavecoeff.append(v_j_1)
    return np.vstack(wavecoeff)


######################################################################
#inicio da minha parte


preco = pd.read_excel('Precos.xlsx',index_col=0)

ret = preco.pct_change(1)


ret = ret.dropna()
ret_ary = np.array(ret)[-3001:]


D = {}
col = 0

for k in ret.columns:
    D[k] = modwt(ret_ary[0:-1, list(ret.columns).index(k)] , 'haar' , 7)


#%%
#writer_list = []
for x in D.keys():

    nme= str.split(str(x), '.')[0]

    writer = pd.ExcelWriter(os.path.join(os.getcwd(),'dados\\%s.xlsx' % nme))
    pd.DataFrame(D[x]).T.to_excel(writer, 'sheet1')
#    writer_list.append(writer)
    writer.save()

#for x in writer_list:
#    writer.save()
#