Change collections.Iterable to collections.abc.Iterable

deltasigma/_axisLabels.py

@@ -51,7 +51,7 @@ def axisLabels(ran, incr):
     ran = np.asarray(ran)
     ran[np.abs(ran) < 1e-6] = 0
     s = []
-    if not isinstance(incr, collections.Iterable):
+    if not isinstance(incr, collections.abc.Iterable):
         incr = int(incr)
         first = 0
     elif len(incr) == 2:

deltasigma/_pulse.py

@@ -80,8 +80,8 @@ def pulse(S, tp=(0., 1.), dt=1., tfinal=10., nosum=False):
     _, df = rat(tfinal, 1e-3)
     delta_t = 1./lcm(dd, lcm(ddt, df))
     delta_t = max(1e-3, delta_t)    # Put a lower limit on delta_t
-    if (isinstance(S, collections.Iterable) and len(S)) \
-       and (isinstance(S[0], collections.Iterable) and len(S[0])) \
+    if (isinstance(S, collections.abc.Iterable) and len(S)) \
+       and (isinstance(S[0], collections.abc.Iterable) and len(S[0])) \
            and (isinstance(S[0][0], lti) or _is_zpk(S[0][0]) or _is_num_den(S[0][0]) \
                 or _is_A_B_C_D(S[0][0])):
         pass

deltasigma/_simulateDSM_python.py

@@ -132,7 +132,7 @@ def simulateDSM(u, arg2, nlev=2, x0=0.):
     # need to set order and form now.
     order = carray(zeros).shape[0] if form == 2 else ABCD.shape[0] - nq
 
-    if not isinstance(x0, collections.Iterable):
+    if not isinstance(x0, collections.abc.Iterable):
         x0 = x0*np.ones((order,), dtype=np.float64)
     else:
         x0 = np.array(x0).reshape((-1,))

deltasigma/_simulateSNR.py

@@ -191,7 +191,7 @@ def simulateSNR(arg1, osr, amp=None, f0=0, nlev=2, f=None, k=13,
         amp = np.concatenate((np.arange(- 120, -20 + 1, 10),
                               np.array((-15,)),
                               np.arange(-10, 1)))
-    elif not isinstance(amp, collections.Iterable):
+    elif not isinstance(amp, collections.abc.Iterable):
         amp = np.array((amp, ))
     else:
         amp = np.asarray(amp)