diff --git a/wetb/signal/filters/despike.py b/wetb/signal/filters/despike.py
index e65a61c9652bea27a0809cceba3302851558e94f..f7256c68b1bd499faf78987afa83b644ff8f4dee 100644
--- a/wetb/signal/filters/despike.py
+++ b/wetb/signal/filters/despike.py
@@ -4,8 +4,8 @@ Created on 13/07/2016
@author: MMPE
'''
import numpy as np
-from wetb.signal_tools.filters.first_order import low_pass
-from wetb.signal_tools.filters import replacer
+from wetb.signal.filters.first_order import low_pass
+from wetb.signal.filters import replacer
replace_by_nan = replacer.replace_by_nan
diff --git a/wetb/signal/filters/first_order.py b/wetb/signal/filters/first_order.py
index 6bcfd04655633e423127d80a4537654aad9385cc..3b5824bd3117e24b4180a075e6d8195f5dcda68c 100644
--- a/wetb/signal/filters/first_order.py
+++ b/wetb/signal/filters/first_order.py
@@ -4,7 +4,7 @@ Created on 10/01/2015
@author: mmpe
'''
import numpy as np
-from wetb.signal_tools.filters import cy_filters
+from wetb.signal.filters import cy_filters
def low_pass(input, delta_t, tau, method=1):
if isinstance(tau, (int, float)):
diff --git a/wetb/signal/fit/_fourier_fit.py b/wetb/signal/fit/_fourier_fit.py
index eee63657160057f351c262111306fbd0b508454b..ae2e9b4ca21ef21eb92705a48ab6d1d19ee24e95 100644
--- a/wetb/signal/fit/_fourier_fit.py
+++ b/wetb/signal/fit/_fourier_fit.py
@@ -58,12 +58,16 @@ def x2F(y, max_nfft, x=None):
F = np.r_[AB[0], (AB[1:nfft + 1] + 1j * AB[nfft + 1:]), np.zeros(nfft) ]
return F
-def rx2F(y, max_fft):
+def rx2F(y, max_nfft, x=None):
"""Convert non-complex signal, y, to single sided Fourier components, that satifies x(t) = sum(X(cos(iw)+sin(iw)), i=0..N)"""
+ d = np.arange(360)
+ if x is not None:
+ x,fit = bin_fit(x,y, d)
+ y = fit(d)
F = np.fft.rfft(y) / len(y)
F[1:-1] *= 2 # add negative side
F = np.conj(F)
- return F[:max_fft + 1]
+ return F[:max_nfft + 1]
def rF2x(rF):
"""Convert single sided Fourier components, that satisfies x(t) = sum(X(cos(iw)+sin(iw)), i=0..N) to non-complex signal"""
diff --git a/wetb/signal/tests/test_first_order.py b/wetb/signal/tests/test_first_order.py
index c49c464d36732c26bcf47a232ecb72ae75940b5c..f550d38cbd316723d6da87a3875aea464465d558 100644
--- a/wetb/signal/tests/test_first_order.py
+++ b/wetb/signal/tests/test_first_order.py
@@ -4,8 +4,13 @@ Created on 13. jan. 2017
@author: mmpe
'''
import unittest
-from wetb.signal_tools.filters import first_order
+
+from scipy import signal
+
import numpy as np
+from wetb.signal.filters import first_order
+from wetb.signal.fit._fourier_fit import F2x, x2F, rx2F
+
class Test_first_order_filters(unittest.TestCase):
@@ -19,6 +24,153 @@ class Test_first_order_filters(unittest.TestCase):
plt.plot(a)
plt.plot(b)
plt.show()
+
+
+ def test_low_pass2(self):
+ t = np.linspace(0, 1.0, 2001)
+ xlow = np.sin(2 * np.pi * 5 * t)
+ xhigh = np.sin(2 * np.pi * 250 * t)
+ x = xlow + xhigh
+
+ b, a = signal.butter(8, 0.125)
+ y = signal.filtfilt(b, a, x, padlen=150)
+ self.assertAlmostEqual(np.abs(y - xlow).max(),0,4)
+ if 0:
+ import matplotlib.pyplot as plt
+ plt.plot(x)
+ plt.plot(y)
+ plt.show()
+
+
+ def test_low_pass3(self):
+ t = np.linspace(0, 1.0, 2001)
+# xlow = np.sin(2 * np.pi * 5 * t)
+# xhigh = np.sin(2 * np.pi * 250 * t)
+# x = xlow + xhigh
+ x = np.sum([np.sin(t*x*2*np.pi) for x in range(1,200)],0)
+ cutoff = .2
+ b, a = signal.butter(8,cutoff)
+ w, h = signal.freqs(b, a)
+ y = signal.filtfilt(b, a, x)
+ F = rx2F(x, max_nfft=len(t))
+ tF = np.linspace(0, len(F)/t[-1],len(F))
+ Fy = rx2F(y, max_nfft=len(t))
+
+ if 1:
+ import matplotlib.pyplot as plt
+# plt.plot(x)
+# plt.plot(y)
+# plt.show()
+
+ plt.plot(tF, np.abs(F))
+ plt.plot(tF, np.abs(Fy))
+ plt.xlim([0,260])
+ plt.show()
+ print (b,a)
+#
+# plt.plot(w, 20 * np.log10(abs(h)))
+# plt.xscale('log')
+# plt.title('Butterworth filter frequency response')
+# plt.xlabel('Frequency [radians / second]')
+# plt.ylabel('Amplitude [dB]')
+# plt.margins(0, 0.1)
+# plt.grid(which='both', axis='both')
+# plt.axvline(cutoff, color='green') # cutoff frequency
+# plt.show()
+#
+# def test_low_pass2(self):
+# t = np.arange(100000)/10000
+# dt = t[1]-t[0]
+# hz2 = np.sin(t*2*2*np.pi) # frq = 2 hz or 2*2pi rad/s
+# hz10 = np.sin(t*10*2*np.pi) # frq = 10 hz or 10*2pi rad/s
+# hz20 = np.sin(t*100*2*np.pi) # frq = 100 hz or 100*2pi rad/s
+# #sig = np.sum([np.sin(t*x*2*np.pi) for x in range(1,200)],0)
+# sig = np.sum([np.sin(t*x*2*np.pi) for x in [1,5,10]],0)
+# print (sig.shape)
+# F = rx2F(sig, max_nfft=len(t))
+# tF = np.linspace(0, len(F)/t[-1],len(F))
+#
+#
+# sig_lp1 = first_order.low_pass(sig, dt, 30*dt)
+# F_lp1 = rx2F(sig_lp1, max_nfft=len(t))
+#
+# bb, ba = signal.butter(1,5, 'low', analog=True)
+# print (bb, ba)
+# w1, h1 = signal.freqs(bb, ba)
+# sig_lp2 = signal.filtfilt(bb,ba, sig)
+# # print (sig_lp2)
+# # F_lp2 = rx2F(sig_lp2, max_nfft=len(t))
+# # bb, ba = signal.butter(10,10, 'low', analog=True)
+# # sig_lp3 = signal.lfilter(bb,ba, sig)
+# # F_lp3 = rx2F(sig_lp3, max_nfft=len(t))
+# # w2, h2 = signal.freqs(bb, ba)
+# # #
+# # self.assertLess(b.std(), a.std())
+# if 1:
+# import matplotlib.pyplot as plt
+# plt.plot(t, sig)
+# # plt.plot(t, np.sum([1/x*np.sin(t*x*2*np.pi) for x in [1]],0))
+# #
+# plt.plot(t, sig_lp1)
+# plt.plot(t, sig_lp2)
+# #plt.plot(t, sig_lp3)
+#
+# plt.show()
+#
+# # plt.plot(tF, np.abs(F))
+# # plt.plot(tF, np.abs(F_lp1))
+# # # plt.plot(tF, np.abs(F_lp3))
+# # # plt.plot(tF, np.abs(F_lp2))
+# # plt.plot([0,1000],[0.708,.708])
+# # plt.xlim([0,205])
+# # plt.ylim([0,1])
+# # plt.show()
+# # plt.plot(w1, 20 * np.log10(abs(h1)))
+# # plt.plot(w2, 20 * np.log10(abs(h2)))
+# # plt.xscale('log')
+# # plt.title('Butterworth filter frequency response')
+# # plt.xlabel('Frequency [radians / second]')
+# # plt.ylabel('Amplitude [dB]')
+# # plt.margins(0, 0.1)
+# # plt.grid(which='both', axis='both')
+# # plt.axvline(100, color='green') # cutoff frequency
+# # plt.show()
+
+
+
+# # def test_low_pass2(self):
+# # F = [0,1,0,0,0,0,0,0,0,0,0,0,0,0,.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,.1]
+# # F +=[0]*(len(F)-1)
+# # x = F2x(F)
+# # a = np.tile(x, 3)
+# # print (x.shape)
+# # # a = np.random.randint(0,100,100).astype(np.float)
+# # b = first_order.low_pass(a, .1, 1)
+# # bb, ba = signal.butter(10,100, 'low', analog=True)
+# # #c = signal.lfilter(bb,ba, a)
+# # w, h = signal.freqs(bb, ba)
+# #
+# # # self.assertLess(b.std(), a.std())
+# # if 1:
+# # import matplotlib.pyplot as plt
+# # plt.plot(a)
+# # #plt.ylim([-2,2])
+# # plt.plot(b)
+# # #plt.plot(c)
+# # plt.show()
+# # print (h)
+# # plt.plot(w, abs(h))
+# # plt.xscale('log')
+# # plt.title('Butterworth filter frequency response')
+# # plt.xlabel('Frequency [radians / second]')
+# # plt.ylabel('Amplitude [dB]')
+# # plt.margins(0, 0.1)
+# # plt.grid(which='both', axis='both')
+# # plt.axvline(100, color='green') # cutoff frequency
+# # plt.show()
+# #
+# #
+
def test_high_pass(self):
a = np.random.randint(0,100,100).astype(np.float)
diff --git a/wetb/signal/tests/test_spectrum.py b/wetb/signal/tests/test_spectrum.py
index fc20a3b8bb1177b401f601af12c8c5a39dc6f31c..8051c0407240956e18943ec5a4366247655fa827 100644
--- a/wetb/signal/tests/test_spectrum.py
+++ b/wetb/signal/tests/test_spectrum.py
@@ -5,7 +5,7 @@ Created on 12/11/2015
'''
import unittest
import numpy as np
-from wetb.signal_tools.spectrum import psd
+from wetb.signal.spectrum import psd
class TestSpectrum(unittest.TestCase):