diff --git a/src/python/mlat-test.py b/src/python/mlat-test.py
index 4ccea51..90868c1 100755
--- a/src/python/mlat-test.py
+++ b/src/python/mlat-test.py
@@ -2,12 +2,22 @@
 import mlat
 import numpy
 
-replies = []
-for i in range(0, len(mlat.teststations)):
-    replies.append((mlat.teststations[i], mlat.teststamps[i]))
+#here's some test data to validate the algorithm
+teststations = [[37.76225, -122.44254, 100], [37.409044, -122.077748, 100], [37.585085, -121.986395, 100]]
+testalt      = 8000
+testplane    = numpy.array(mlat.llh2ecef([37.617175,-122.380843, testalt]))
+testme       = mlat.llh2geoid(teststations[0])
+teststamps   = [10, 
+                10 + numpy.linalg.norm(testplane-numpy.array(mlat.llh2geoid(teststations[1]))) / mlat.c,
+                10 + numpy.linalg.norm(testplane-numpy.array(mlat.llh2geoid(teststations[2]))) / mlat.c,
+               ]
 
-ans = mlat.mlat(replies, mlat.testalt)
-error = numpy.linalg.norm(numpy.array(mlat.llh2ecef(ans))-numpy.array(mlat.testplane))
-range = numpy.linalg.norm(mlat.llh2geoid(ans)-numpy.array(mlat.llh2geoid(mlat.teststations[0])))
+replies = []
+for i in range(0, len(teststations)):
+    replies.append((teststations[i], teststamps[i]))
+
+ans = mlat.mlat(replies, testalt)
+error = numpy.linalg.norm(numpy.array(mlat.llh2ecef(ans))-numpy.array(testplane))
+range = numpy.linalg.norm(mlat.llh2geoid(ans)-numpy.array(mlat.llh2geoid(teststations[0])))
 print "Error: %.2fm" % (error)
 print "Range: %.2fkm (from first station in list)" % (range/1000)
diff --git a/src/python/mlat.py b/src/python/mlat.py
index c410d83..848080f 100755
--- a/src/python/mlat.py
+++ b/src/python/mlat.py
@@ -147,16 +147,6 @@ def llh2geoid((lat, lon, alt)):
 
 c = 299792458 / 1.0003 #modified for refractive index of air, why not
 
-#here's some test data to validate the algorithm
-teststations = [[37.76225, -122.44254, 100], [37.409044, -122.077748, 100], [37.585085, -121.986395, 100]]
-testalt      = 8000
-testplane    = numpy.array(llh2ecef([37.617175,-122.380843, testalt]))
-testme       = llh2geoid(teststations[0])
-teststamps   = [10, 
-                10 + numpy.linalg.norm(testplane-numpy.array(llh2geoid(teststations[1]))) / c,
-                10 + numpy.linalg.norm(testplane-numpy.array(llh2geoid(teststations[2]))) / c,
-               ]
-
 #this function is the iterative solver core of the mlat function below
 #we use limit as a goal to stop solving when we get "close enough" (error magnitude in meters for that iteration)
 #basically 20 meters is way less than the anticipated error of the system so it doesn't make sense to continue
@@ -174,7 +164,6 @@ def mlat_iter(rel_stations, prange_obs, xguess = [0,0,0], limit = 20, maxrounds
             H.append((numpy.array(-rel_stations[row,:])-xguess) / prange_est[row])
         H = numpy.array(H)
         #now we have H, the Jacobian, and can solve for residual error
-        #xerr = numpy.dot(numpy.linalg.solve(numpy.dot(H.T,H), H.T), dphat).flatten()
         xerr = numpy.linalg.lstsq(H, dphat)[0].flatten() #let's not get crazy here
         xguess += xerr
         rounds += 1