dsp: remove unused code

amodem/dsp.py

@@ -4,6 +4,20 @@ from numpy import linalg
 from . import common
 
 
+class FIR(object):
+    def __init__(self, h):
+        self.h = np.array(h)
+        self.x_state = [0] * len(self.h)
+
+    def __call__(self, x):
+        x_ = self.x_state
+        h = self.h
+        for v in x:
+            x_ = [v] + x_[:-1]
+            yield np.dot(x_, h)
+        self.x_state = x_
+
+
 class IIR(object):
     def __init__(self, b, a):
         self.b = np.array(b) / a[0]
@@ -24,41 +38,12 @@ class IIR(object):
         self.x_state, self.y_state = x_, y_
 
 
-class FIR(object):
-    def __init__(self, h):
-        self.h = np.array(h)
-        self.x_state = [0] * len(self.h)
-
-    def __call__(self, x):
-        x_ = self.x_state
-        h = self.h
-        for v in x:
-            x_ = [v] + x_[:-1]
-            yield np.dot(x_, h)
-        self.x_state = x_
-
-
 def lfilter(b, a, x):
     f = IIR(b=b, a=a)
     y = list(f(x))
     return np.array(y)
 
 
-def estimate(x, y, order, lookahead=0):
-    offset = order - 1
-    assert offset >= lookahead
-    b = y[offset-lookahead:len(x)-lookahead]
-
-    A = []  # columns of x
-    N = len(x) - order + 1
-    for i in range(order):
-        A.append(x[i:N+i])
-
-    # switch to rows for least-squares
-    h = linalg.lstsq(np.array(A).T, b)[0]
-    return h[::-1]
-
-
 class Demux(object):
     def __init__(self, sampler, omegas, Nsym):
         self.Nsym = Nsym

tests/test_dsp.py

@@ -29,36 +29,6 @@ def test_filter():
     assert list(y) == [0.5 ** (i+1) for i in range(len(x))]
 
 
-def test_estimate():
-    r = np.random.RandomState(seed=0)
-    x = r.uniform(-1, 1, [1000])
-    x[:10] = 0
-    x[len(x)-10:] = 0
-
-    c = 1.23
-    y = c * x
-    c_, = dsp.estimate(x=x, y=y, order=1)
-    assert abs(c - c_) < 1e-12
-
-    h = [1, 1]
-    y = dsp.lfilter(b=h, a=[1], x=x)
-    h_ = dsp.estimate(x=x, y=y, order=len(h))
-    assert norm(h - h_) < 1e-12
-
-    h = [0.1, 0.6, 0.9, 0.7, -0.2]
-    L = len(h) // 2
-
-    y = dsp.lfilter(b=h, a=[1], x=x)
-    h_ = dsp.estimate(
-        x=x[:len(x)-L], y=y[L:],
-        order=len(h), lookahead=L
-    )
-    assert norm(h - h_) < 1e-12
-
-    y_ = dsp.lfilter(b=h_, a=[1], x=x)
-    assert norm(y - y_) < 1e-12
-
-
 def test_demux():
     freqs = np.array([1e3, 2e3])
     omegas = 2 * np.pi * freqs / config.Fs