def test_cy_py_same():
signal = np.arange(20, dtype=np.float32)
cy_result = agc_cython.agc(4, signal)
py_result = agc_python.agc(4, signal)
c_cy_result = agc_c_cy.agc(4, signal)
# sub_result = agc_subroutine.agc(4, signal)
print "cy:", cy_result
print "py:", py_result
print "c_cy", c_cy_result
# print "subroutine", sub_result
assert np.array_equal(cy_result, py_result)
assert np.array_equal(cy_result, c_cy_result)
def test_cy_py_same():
signal = np.arange(20, dtype=np.float32)
cy_result = agc_cython.agc(4, signal)
py_result = agc_python.agc(4, signal)
c_cy_result = agc_c_cy.agc(4, signal)
# sub_result = agc_subroutine.agc(4, signal)
print "cy:", cy_result
print "py:", py_result
print "c_cy", c_cy_result
# print "subroutine", sub_result
assert np.array_equal(cy_result, py_result)
assert np.array_equal(cy_result, c_cy_result)
t = np.linspace(0,20,100).astype(np.float32) signal = np.sin(t) # add some noise signal += (np.random.random(signal.shape)-0.5) * 0.3 # create an array for the result: #filtered = np.zeros_like(signal) # run it through the AGC filter: filtered = agc_subroutine.agc(10, signal) # try the python version filtered2 = agc_python.agc(10, signal) if np.allclose(filtered2, filtered2): print "the same" else: print "not the same" ## plot the results fig = plt.figure(figsize=(10,5)) ax = fig.add_subplot(1,1,1) ax.plot(t, signal, t, filtered, t, filtered2) plt.show()