"""
Filters for time series.

Copyright (c) 2020 Gabriele Gilardi

ToDo:
- add comments to the code
- in comments write what filters do
- is necessary to copy X for Y untouched?
- decide default values in functions
- why lag plot gives errors
- fix plotting function
- example for alpha-beta-gamma using variable sigma as in financial time series
  (see Ehler)
- example using noisy multi-sine-waves
- vectors must be ( .., 1)
- synt remove multi-series unless multi-variate and add number of istances (fft 3 D)
"""

import sys
import numpy as np
import matplotlib.pyplot as plt

import filters as flt
import synthetic as syn

# Read data to filter
if len(sys.argv) != 2:
    print("Usage: python test.py <data_file>")
    sys.exit(1)
data_file = sys.argv[1] + '.csv'

# Read data from a csv file
data = np.loadtxt(data_file, delimiter=',')
n_samples = data.shape[0]
data = data.reshape(n_samples, -1)

np.random.seed(1294404794)

# spx = flt.Filter(data)

# res, bb, aa = spx.SincFunction(2, 50)
# print(bb)
# print(aa)
# utl.plot_frequency_response(bb, aa)
# utl.plot_lag_response(bb, aa)
# sigma_x = 0.1
# sigma_v = 0.1 * np.ones(n_samples)
# res = spx.Kalman(sigma_x=sigma_x, sigma_v=sigma_v, dt=1.0, abg_type="abg")
# alpha = 0.5
# beta = 0.005
# gamma = 0.0
# Yc, Yp = spx.ABG(alpha=alpha, beta=beta, gamma=gamma, dt=1.0)
# signals = (spx.data[:, 0], Yc[:, 0], Yp[:, 0])
# utl.plot_signals(signals, 0, 50)

# t, f = syn.synthetic_wave([1., 2., 3.], A=None, phi=None, num=100)
# plt.plot(t,f)
# plt.show()
# aa = np.array([
#         [ 0.8252,  0.2820],
#         [ 1.3790,  0.0335],
#         [-1.0582, -1.3337],
#         [-0.4686,  1.1275],
#         [-0.2725,  0.3502],
#         [ 1.0984, -0.2991],
#         [-0.2779,  0.0229],
#         [ 0.7015, -0.2620],
#         [-2.0518, -1.7502],
#         [-0.3538, -0.2857],
#         [-0.8236, -0.8314],
#         [-1.5771, -0.9792],
#         [ 0.5080, -1.1564]])
# synt_data1 = syn.synthetic_FFT(data, False)
# synt_data2 = syn.synthetic_FFT(data, False)
# plt.plot(synt_data1)
# plt.plot(synt_data2)
# plt.plot(data)
# names = ['syn1', 'syn2', 'spx']
# plt.legend(names)
# plt.show()
# percent = False
# print(data[0:10, :])
# bb = syn.value2diff(data, percent)
# print(bb[0:10, :])
# cc = syn.diff2value(bb, percent)
# print(cc[0:10, :]+1399.48)
# aa = np.arange(10,28).reshape(6,3)
# print(aa)
# idx = np.zeros_like(aa)
# bb = np.zeros_like(aa)
# for i in range(aa.shape[1]):
#     idx[:, i] = np.random.permutation(aa.shape[0])
# print(idx)
# i = np.arange(aa.shape[1])
# bb[:, i] = aa[idx[:, i], i]
# bb = syn.synthetic_boot(aa, replace=False)
# print(bb)
# aa = np.array([4, 12, 36, 20, 8]).reshape(5, 1)
# W = syn.synthetic_MEboot(aa, alpha=0.1, bounds=False, scale=True)
# print(bb.sum())
# print('W=', W)

n = 8
aa = np.arange(n).reshape(n,1) * 1.1
print(aa)
idx = np.random.randint(0, n, size=(n, 3))
print(idx)
i = np.arange(3)
# print(i)
bb = np.tile(aa,(1, 3))
# print(bb)
cc = bb[idx, i]
print(cc)