diff --git a/ttide/t_predic.py b/ttide/t_predic.py
index fede2f8..17e9f9d 100644
--- a/ttide/t_predic.py
+++ b/ttide/t_predic.py
@@ -96,7 +96,11 @@ def t_predic(t_time, names, freq, tidecon,
         ju = np.zeros((len(freq),), dtype='int32')
         # Check to make sure names and frequencies match expected values.
         for k in range(0, (names.shape[0])):
-            ju[k] = np.argwhere(const['name'] == names[(k)])
+            matches = np.flatnonzero(const['name'] == names[(k)])
+            if matches.size == 0:
+                raise ValueError("Constituent name '%s' not found in constants" %
+                                 (names[(k)],))
+            ju[k] = matches[0]
         # if any(freq~=const.freq(ju)),
         # error('Frequencies do not match names in input');
         # end;
diff --git a/ttide/t_tide.py b/ttide/t_tide.py
index 130ccc2..7be9cf2 100644
--- a/ttide/t_tide.py
+++ b/ttide/t_tide.py
@@ -98,8 +98,8 @@ def t_tide(xin, dt=1, stime=None, lat=None,
                 series will remain and mean/trend are included).
 
     lsq : string
-        'direct'  - use A\ x fit
-        'normal'  - use (A'A)\(A'x) (may be necessary for very large
+        'direct'  - use A\\ x fit
+        'normal'  - use (A'A)\\(A'x) (may be necessary for very large
                   input vectors since A'A is much smaller than A)
           'best'  - automatically choose based on length of series
                   (default).
@@ -427,14 +427,14 @@ def t_tide(xin, dt=1, stime=None, lat=None,
     # would be to change the basis functions.
     ####################################################################
     ii = np.flatnonzero(np.isfinite(jref))
-    if ii:
+    if ii.size > 0:
         print('   Do inference corrections\\n')
         snarg = nobsu * pi * dt * (fi[(ii - 1)] - fu[(jref[(ii - 1)] - 1)])
         scarg = np.sin(snarg) / snarg
         if infamprat.shape[1] == 1:
             # For real time series
-            pearg = np.dot(2 * pi,ii
-                           (vu[(mu + ii - 1)] -
+            pearg = np.dot(2 * pi, ii * (
+                           vu[(mu + ii - 1)] -
                             vu[(jref[(ii - 1)] - 1)] +
                             infph[(ii - 1)])) / 360
             pcfac = infamprat[(ii - 1)] * f[(mu + ii - 1)] / \
diff --git a/ttide/tests/print_tests.py b/ttide/tests/print_tests.py
index 71f06d9..cf6633c 100644
--- a/ttide/tests/print_tests.py
+++ b/ttide/tests/print_tests.py
@@ -1,6 +1,6 @@
 from ttide.t_tide import t_tide
 import ttide.tests.base as bmod
-from cStringIO import StringIO
+from io import StringIO
 import sys
 import numpy as np