示例#1
0
文件: Noise.py 项目: tsouvarev/mining
def reverse_filter (d, t = "ideal", N = None, v = None):

	d_f = fourier (d)
	g = empty ((0,4))

	if t == "ideal": 
	
		a = 0.00001
		
		for r, i in zip(d_f[:,re], d_f[:,im]):

			#m = 1 / (r+a if r != 0 else 1500)
			#n = 1 / (i+a if i != 0 else 1500)
			m = r / (r*r+i*i)
			n = -i / (r*r+i*i)
			
			g = concatenate ((g, [[m, n, sqrt (m*m + n*n), m + n]]))		
	
	elif t == "regular": 
	
		alpha = 0.4
	
		# находим обратный фильтр
		for r, i in zip(d_f[:,re], d_f[:,im]):

			m = r / ( r*r + i*i + alpha*alpha ) 
			n = -i / ( r*r + i*i + alpha*alpha ) 
			g = concatenate ((g, [[m, n, sqrt (m*m + n*n), m + n]]))
					
	elif t == "viner":
	
		if N == None or v == None: return None
	
		N = fourier (N)
		v = fourier (v)
	
		# находим обратный фильтр
		for r, i, n_r, n_i, v_r, v_i in zip(d_f[:,re], d_f[:,im], N[:,re], N[:,im], v[:,re], v[:,im]):
		
			b = 1/(v_r*v_r + v_i*v_i)
			
			n_n = n_r*n_r + n_i*n_i

			m = r / ( r*r + i*i + b*n_n ) 
			n = -i / ( r*r + i*i + b*n_n ) 
			
			g = concatenate ((g, [[m, n, sqrt (m*m + n*n), m + n]]))	
	
#	print g
	
	return g
示例#2
0
文件: Noise.py 项目: tsouvarev/mining
def init ():

	#x = array ([2*(gauss(0,1)-0.5) for i in range (N)])
	u = zeros(N); s = zeros(N); v = array([])
	beats = {
				"200": 120, "400": 125, "600": 119, "800": 130,
				"240": -80, "440": -75, "640": -70, "840": -85
			}

	# генерируем сердцебиение
	
	for b in beats: u[int(b)] = beats[b]

	# генерируем синусоиду и дополняем ее нулями

	d = zeros (N)	
#	d[:L] = [sin (2 * pi * f * t * 0.008) * exp (-alpha * t * 0.008) for t in range (L)]
	dt = 1; m = 64; n = 10


	d  = [  sin (2 * pi * 1 * t * 0.008 + 10*pi) 
#			+ sin (2 * pi * 17 * t * 0.008) 
#			+ sin (2 * pi * 150 * t * 0.008) 
			for t in range (N)]
			
	a = array ([lfsr().next() for i in range (N)])*5
	a_f = fourier (a)
	bpf_f = fourier (bpf (0.1, 0.2, L = N, m = m))
	a_short_f = conv_freq (a_f, bpf_f)
	a_short = fourier_inv (a_short_f[:,comp])
	
	#d_n = conv (d, a_short)
#	
	html.add_figure (d, u"Синусоида")
#	html.add_figure (add_spikes (-1, 1, 30, d), u"Синусоида с импульсами")
#	html.add_figure (add_trend (linear_trend (0.001, 0.01, 2, len(d)), d), u"Синусоида с линейным трендом")
#	html.add_figure (add_trend (exp_trend (0.001, 0.01, len(d)), d), u"Синусоида с экспоненциальным трендом")

	html.add_break ()		
	
	d = zeros (N)
	d[:L] = [sin (2 * pi * f * t * 0.008) * exp (-alpha * t * 0.008) for t in range (L)]
	
	v = conv (u,d)
	v_n = v + a
	
	html.add_figure (u, u"Кардиограмма")
#	html.add_figure (d, u"Синусоида")
	html.add_figure (v, u"Свертка")
	html.add_figure (v_n, u"Свертка с шумом")
	
	v_f = fourier (v)
	v_n_f = fourier (v_n)

	d_f = fourier (d)
	
#	g_i = reverse_filter (d, "ideal", a, v)
	g_r = reverse_filter (d, "regular", a, v)
#	g_v = reverse_filter (d+a, "viner", a, v)
	
#	html.add_figure (g_i[:,comp], u"Обратный идеальный фильтр\n(комплексный спектр)")
	html.add_figure (g_r[:,comp], u"Обратный регулярный фильтр\n(комплексный спектр)")
#	html.add_figure (g_v[:,comp], u"Обратный винеровский фильтр\n(комплексный спектр)")
#	
#	g_f = conv_freq (g_i, v_f)
#	d_est = fourier_inv (g_f[:,comp])
#	html.add_figure (d_est, u"Деконволюция идеальным фильтром")
	
	g_f = conv_freq (g_r, v_f)
	d_est = fourier_inv (g_f[:,comp])
	html.add_figure (d_est, u"Деконволюция регулярным фильтром")