diff --git a/.travis.yml b/.travis.yml
index 59293df0..b0327361 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -3,13 +3,26 @@ sudo: false
 branches:
 only:
   - master
-python:
-  - "3.5"
-  - "3.6"
 
-env:
-  - PYSAL_PLUS=false
-  - PYSAL_PLUS=true
+python:
+    - 3.5
+    - 3.6
+env: 
+    - PYSAL_PYPI=true PYSAL_PLUS=true
+    - PYSAL_PYPI=true PYSAL_PLUS=false
+    - PYSAL_PYPI=false PYSAL_PLUS=true
+    - PYSAL_PYPI=false PYSAL_PLUS=false
+      
+matrix:
+  allow_failures:
+      - python: 3.5
+        env: PYSAL_PYPI=false PYSAL_PLUS=false
+      - python: 3.5
+        env: PYSAL_PYPI=false PYSAL_PLUS=true
+      - python: 3.6
+        env: PYSAL_PYPI=false PYSAL_PLUS=false
+      - python: 3.6
+        env: PYSAL_PYPI=false PYSAL_PLUS=true
 
 before_install:
   - wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
@@ -23,9 +36,12 @@ before_install:
 install:
   - conda install --yes pip nose
   - which pip
+  - if "$PYSAL_PYPI"; then
+        echo 'testing pypi libpysal' && pip install libpysal;
+        else echo 'testing git libpysal'; git clone https://github.com/pysal/libpysal.git; cd libpysal; pip install .; cd ../;
+    fi;
   - conda install --yes --file requirements.txt;
-  - pip install libpysal
-  - if [[ PYSAL_PLUS ]]; then conda install --yes numba; fi
+  - if "$PYSAL_PLUS"; then conda install --yes numba; fi
 
 script:
   - pwd
@@ -38,8 +54,8 @@ notifications:
         recipients:
             - levi.john.wolf+travis@gmail.com
             - sjsrey+travis@gmail.com
-        on_change: always
-        on_failure: always
+        on_success: change
+        on_failure: change
 
 after_success:
   - coveralls
diff --git a/esda/gamma.py b/esda/gamma.py
index f6f38b19..b897bafd 100644
--- a/esda/gamma.py
+++ b/esda/gamma.py
@@ -75,9 +75,9 @@ class Gamma(object):
 
     use same example as for join counts to show similarity
 
-    >>> import libpysal.api as lps, numpy as np
+    >>> import libpysal, numpy as np
     >>> from esda.gamma import Gamma
-    >>> w = lps.lat2W(4,4)
+    >>> w = libpysal.weights.lat2W(4,4)
     >>> y=np.ones(16)
     >>> y[0:8]=0
     >>> np.random.seed(12345)
diff --git a/esda/geary.py b/esda/geary.py
index 0d69338d..6518178f 100644
--- a/esda/geary.py
+++ b/esda/geary.py
@@ -80,10 +80,10 @@ class Geary(object):
 
     Examples
     --------
-    >>> import libpysal.api as lps
+    >>> import libpysal
     >>> from esda.geary import Geary
-    >>> w = lps.open(lps.get_path("book.gal")).read()
-    >>> f = lps.open(lps.get_path("book.txt"))
+    >>> w = libpysal.io.open(libpysal.examples.get_path("book.gal")).read()
+    >>> f = libpysal.io.open(libpysal.examples.get_path("book.txt"))
     >>> y = np.array(f.by_col['y'])
     >>> c = Geary(y,w,permutations=0)
     >>> round(c.C,7)
diff --git a/esda/getisord.py b/esda/getisord.py
index e0ce0af6..5a312f95 100644
--- a/esda/getisord.py
+++ b/esda/getisord.py
@@ -4,7 +4,7 @@
 __author__ = "Sergio J. Rey <srey@asu.edu>, Myunghwa Hwang <mhwang4@gmail.com> "
 __all__ = ['G', 'G_Local']
 
-from libpysal.common import np, stats, math
+from libpysal.common import np, stats 
 from libpysal.weights.spatial_lag import lag_spatial as slag
 from .tabular import _univariate_handler
 
@@ -67,7 +67,7 @@ class G(object):
 
     Examples
     --------
-    >>> import libpysal.api as lps
+    >>> import libpysal
     >>> import numpy
     >>> numpy.random.seed(10)
 
@@ -75,7 +75,7 @@ class G(object):
     >>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
 
     Creating a weights object from points
-    >>> w = lps.DistanceBand(points,threshold=15)
+    >>> w = libpysal.weights.DistanceBand(points,threshold=15)
     >>> w.transform = "B"
 
     Preparing a variable
@@ -106,7 +106,7 @@ def __init__(self, y, w, permutations=PERMUTATIONS):
         y = y.reshape(len(y), 1)  # Ensure that y is an n by 1 vector, otherwise y*y.T == y*y
         self.den_sum = (y * y.T).sum() - (y * y).sum()
         self.G = self.__calc(self.y)
-        self.z_norm = (self.G - self.EG) / math.sqrt(self.VG)
+        self.z_norm = (self.G - self.EG) / np.sqrt(self.VG)
         self.p_norm = 1.0 - stats.norm.cdf(np.abs(self.z_norm))
 
         if permutations:
@@ -278,7 +278,7 @@ class G_Local(object):
 
     Examples
     --------
-    >>> import libpysal.api as lps
+    >>> import libpysal
     >>> import numpy
     >>> numpy.random.seed(10)
 
@@ -288,7 +288,7 @@ class G_Local(object):
 
     Creating a weights object from points
 
-    >>> w = lps.DistanceBand(points,threshold=15)
+    >>> w = libpysal.weights.DistanceBand(points,threshold=15)
 
     Prepareing a variable
 
diff --git a/esda/join_counts.py b/esda/join_counts.py
index 0d36e065..330afab0 100644
--- a/esda/join_counts.py
+++ b/esda/join_counts.py
@@ -79,8 +79,8 @@ class Join_Counts(object):
     Replicate example from anselin and rey
 
     >>> import numpy as np
-    >>> import libpysal.api as lps
-    >>> w = lps.lat2W(4, 4)
+    >>> import libpysal
+    >>> w = libpysal.weights.lat2W(4, 4)
     >>> y = np.ones(16)
     >>> y[0:8] = 0
     >>> np.random.seed(12345)
diff --git a/esda/moran.py b/esda/moran.py
index 32c67b54..5be1ee27 100644
--- a/esda/moran.py
+++ b/esda/moran.py
@@ -98,9 +98,9 @@ class Moran(object):
 
     Examples
     --------
-    >>> import libpysal.api as lps
-    >>> w = lps.open(lps.get_path("stl.gal")).read()
-    >>> f = lps.open(lps.get_path("stl_hom.txt"))
+    >>> import libpysal
+    >>> w = libpysal.io.open(libpysal.examples.get_path("stl.gal")).read()
+    >>> f = libpysal.io.open(libpysal.examples.get_path("stl_hom.txt"))
     >>> y = np.array(f.by_col['HR8893'])
     >>> from esda.moran import Moran
     >>> mi = Moran(y,  w)
@@ -112,8 +112,8 @@ class Moran(object):
     0.00027147862770937614
 
     SIDS example replicating OpenGeoda
-    >>> w = lps.open(lps.get_path("sids2.gal")).read()
-    >>> f = lps.open(lps.get_path("sids2.dbf"))
+    >>> w = libpysal.io.open(libpysal.examples.get_path("sids2.gal")).read()
+    >>> f = libpysal.io.open(libpysal.examples.get_path("sids2.dbf"))
     >>> SIDR = np.array(f.by_col("SIDR74"))
     >>> mi = Moran(SIDR,  w)
     >>> round(mi.I, 3)
@@ -327,7 +327,7 @@ class Moran_BV(object):
 
     Examples
     --------
-    >>> import libpysal.api as lps
+    >>> import libpysal
     >>> import numpy as np
 
     Set random number generator seed so we can replicate the example
@@ -337,13 +337,13 @@ class Moran_BV(object):
     Open the sudden infant death dbf file and read in rates for 74 and 79
     converting each to a numpy array
 
-    >>> f = lps.open(lps.get_path("sids2.dbf"))
+    >>> f = libpysal.io.open(libpysal.examples.get_path("sids2.dbf"))
     >>> SIDR74 = np.array(f.by_col['SIDR74'])
     >>> SIDR79 = np.array(f.by_col['SIDR79'])
 
     Read a GAL file and construct our spatial weights object
 
-    >>> w = lps.open(lps.get_path("sids2.gal")).read()
+    >>> w = libpysal.io.open(libpysal.examples.get_path("sids2.gal")).read()
 
     Create an instance of Moran_BV
     >>> from esda.moran import Moran_BV
@@ -481,8 +481,8 @@ def Moran_BV_matrix(variables, w, permutations=0, varnames=None):
 
     open dbf
 
-    >>> import libpysal.api as lps
-    >>> f = lps.open(lps.get_path("sids2.dbf"))
+    >>> import libpysal
+    >>> f = libpysal.io.open(libpysal.examples.get_path("sids2.dbf"))
 
     pull of selected variables from dbf and create numpy arrays for each
 
@@ -491,7 +491,7 @@ def Moran_BV_matrix(variables, w, permutations=0, varnames=None):
 
     create a contiguity matrix from an external gal file
 
-    >>> w = lps.open(lps.get_path("sids2.gal")).read()
+    >>> w = libpysal.io.open(libpysal.examples.get_path("sids2.gal")).read()
 
     create an instance of Moran_BV_matrix
 
@@ -612,9 +612,9 @@ class Moran_Rate(Moran):
 
     Examples
     --------
-    >>> import libpysal.api as lps
-    >>> w = lps.open(lps.get_path("sids2.gal")).read()
-    >>> f = lps.open(lps.get_path("sids2.dbf"))
+    >>> import libpysal
+    >>> w = libpysal.io.open(libpysal.examples.get_path("sids2.gal")).read()
+    >>> f = libpysal.io.open(libpysal.examples.get_path("sids2.dbf"))
     >>> e = np.array(f.by_col('SID79'))
     >>> b = np.array(f.by_col('BIR79'))
     >>> from esda.moran import Moran_Rate
@@ -788,11 +788,11 @@ class Moran_Local(object):
 
     Examples
     --------
-    >>> import libpysal.api as lps
+    >>> import libpysal
     >>> import numpy as np
     >>> np.random.seed(10)
-    >>> w = lps.open(lps.get_path("desmith.gal")).read()
-    >>> f = lps.open(lps.get_path("desmith.txt"))
+    >>> w = libpysal.io.open(libpysal.examples.get_path("desmith.gal")).read()
+    >>> f = libpysal.io.open(libpysal.examples.get_path("desmith.txt"))
     >>> y = np.array(f.by_col['z'])
     >>> from esda.moran import Moran_Local
     >>> lm = Moran_Local(y, w, transformation = "r", permutations = 99)
@@ -1020,11 +1020,11 @@ class Moran_Local_BV(object):
 
     Examples
     --------
-    >>> import libpysal.api as lps
+    >>> import libpysal
     >>> import numpy as np
     >>> np.random.seed(10)
-    >>> w = lps.open(lps.get_path("sids2.gal")).read()
-    >>> f = lps.open(lps.get_path("sids2.dbf"))
+    >>> w = libpysal.io.open(libpysal.examples.get_path("sids2.gal")).read()
+    >>> f = libpysal.io.open(libpysal.examples.get_path("sids2.dbf"))
     >>> x = np.array(f.by_col['SIDR79'])
     >>> y = np.array(f.by_col['SIDR74'])
     >>> from esda.moran import Moran_Local_BV
@@ -1265,11 +1265,11 @@ class Moran_Local_Rate(Moran_Local):
 
     Examples
     --------
-    >>> import libpysal.api as lps
+    >>> import libpysal
     >>> import numpy as np
     >>> np.random.seed(10)
-    >>> w = lps.open(lps.get_path("sids2.gal")).read()
-    >>> f = lps.open(lps.get_path("sids2.dbf"))
+    >>> w = libpysal.io.open(libpysal.examples.get_path("sids2.gal")).read()
+    >>> f = libpysal.io.open(libpysal.examples.get_path("sids2.dbf"))
     >>> e = np.array(f.by_col('SID79'))
     >>> b = np.array(f.by_col('BIR79'))
     >>> from esda.moran import Moran_Local_Rate
diff --git a/esda/smoothing.py b/esda/smoothing.py
index 0ba914bf..e0777cb2 100644
--- a/esda/smoothing.py
+++ b/esda/smoothing.py
@@ -645,8 +645,8 @@ class Excess_Risk(_Smoother):
 
     Reading data in stl_hom.csv into stl to extract values
     for event and population-at-risk variables
-    >>> import libpysal.api as lps
-    >>> stl = lps.open(lps.get_path('stl_hom.csv'), 'r')
+    >>> import libpysal
+    >>> stl = libpysal.io.open(libpysal.examples.get_path('stl_hom.csv'), 'r')
 
     The 11th and 14th columns in stl_hom.csv includes the number of homocides and population.
     Creating two arrays from these columns.
@@ -700,8 +700,8 @@ class Empirical_Bayes(_Smoother):
     Reading data in stl_hom.csv into stl to extract values
     for event and population-at-risk variables
 
-    >>> import libpysal.api as lps
-    >>> stl = lps.open(lps.get_path('stl_hom.csv'), 'r')
+    >>> import libpysal
+    >>> stl = libpysal.io.open(libpysal.examples.get_path('stl_hom.csv'), 'r')
 
     The 11th and 14th columns in stl_hom.csv includes the number of homocides and population.
     Creating two arrays from these columns.
@@ -844,8 +844,8 @@ class Spatial_Empirical_Bayes(_Spatial_Smoother):
     Reading data in stl_hom.csv into stl to extract values
     for event and population-at-risk variables
 
-    >>> import libpysal.api as lps
-    >>> stl = lps.open(lps.get_path('stl_hom.csv'), 'r')
+    >>> import libpysal
+    >>> stl = libpysal.io.open(libpysal.examples.get_path('stl_hom.csv'), 'r')
 
     The 11th and 14th columns in stl_hom.csv includes the number of homocides and population.
     Creating two arrays from these columns.
@@ -854,7 +854,7 @@ class Spatial_Empirical_Bayes(_Spatial_Smoother):
 
     Creating a spatial weights instance by reading in stl.gal file.
 
-    >>> stl_w = lps.open(lps.get_path('stl.gal'), 'r').read()
+    >>> stl_w = libpysal.io.open(libpysal.examples.get_path('stl.gal'), 'r').read()
 
     Ensuring that the elements in the spatial weights instance are ordered
     by the given sequential numbers from 1 to the number of observations in stl_hom.csv
@@ -924,8 +924,8 @@ class Spatial_Rate(_Spatial_Smoother):
     Reading data in stl_hom.csv into stl to extract values
     for event and population-at-risk variables
 
-    >>> import libpysal.api as lps
-    >>> stl = lps.open(lps.get_path('stl_hom.csv'), 'r')
+    >>> import libpysal
+    >>> stl = libpysal.io.open(libpysal.examples.get_path('stl_hom.csv'), 'r')
 
     The 11th and 14th columns in stl_hom.csv includes the number of homocides and population.
     Creating two arrays from these columns.
@@ -934,7 +934,7 @@ class Spatial_Rate(_Spatial_Smoother):
 
     Creating a spatial weights instance by reading in stl.gal file.
 
-    >>> stl_w = lps.open(lps.get_path('stl.gal'), 'r').read()
+    >>> stl_w = libpysal.io.open(libpysal.examples.get_path('stl.gal'), 'r').read()
 
     Ensuring that the elements in the spatial weights instance are ordered
     by the given sequential numbers from 1 to the number of observations in stl_hom.csv
@@ -1229,8 +1229,8 @@ class Disk_Smoother(_Spatial_Smoother):
     Reading data in stl_hom.csv into stl to extract values
     for event and population-at-risk variables
 
-    >>> import libpysal.api as lps
-    >>> stl = lps.open(lps.get_path('stl_hom.csv'), 'r')
+    >>> import libpysal
+    >>> stl = libpysal.io.open(libpysal.examples.get_path('stl_hom.csv'), 'r')
 
     The 11th and 14th columns in stl_hom.csv includes the number of homocides and population.
     Creating two arrays from these columns.
@@ -1239,7 +1239,7 @@ class Disk_Smoother(_Spatial_Smoother):
 
     Creating a spatial weights instance by reading in stl.gal file.
 
-    >>> stl_w = lps.open(lps.get_path('stl.gal'), 'r').read()
+    >>> stl_w = libpysal.io.open(libpysal.examples.get_path('stl.gal'), 'r').read()
 
     Ensuring that the elements in the spatial weights instance are ordered
     by the given sequential numbers from 1 to the number of observations in stl_hom.csv
@@ -1308,8 +1308,8 @@ class Spatial_Median_Rate(_Spatial_Smoother):
     Reading data in stl_hom.csv into stl to extract values
     for event and population-at-risk variables
 
-    >>> import libpysal.api as lps
-    >>> stl = lps.open(lps.get_path('stl_hom.csv'), 'r')
+    >>> import libpysal
+    >>> stl = libpysal.io.open(libpysal.examples.get_path('stl_hom.csv'), 'r')
 
     The 11th and 14th columns in stl_hom.csv includes the number of homocides and population.
     Creating two arrays from these columns.
@@ -1318,7 +1318,7 @@ class Spatial_Median_Rate(_Spatial_Smoother):
 
     Creating a spatial weights instance by reading in stl.gal file.
 
-    >>> stl_w = lps.open(lps.get_path('stl.gal'), 'r').read()
+    >>> stl_w = libpysal.io.open(libpysal.examples.get_path('stl.gal'), 'r').read()
 
     Ensuring that the elements in the spatial weights instance are ordered
     by the given sequential numbers from 1 to the number of observations in stl_hom.csv
@@ -1437,8 +1437,8 @@ class Spatial_Filtering(_Smoother):
     Reading data in stl_hom.csv into stl to extract values
     for event and population-at-risk variables
 
-    >>> import libpysal.api as lps
-    >>> stl = lps.open(lps.get_path('stl_hom.csv'), 'r')
+    >>> import libpysal
+    >>> stl = libpysal.io.open(libpysal.examples.get_path('stl_hom.csv'), 'r')
 
     Reading the stl data in the WKT format so that
     we can easily extract polygon centroids
@@ -1613,12 +1613,12 @@ class Headbanging_Triples(object):
 
     importing k-nearest neighbor weights creator
 
-    >>> import libpysal.api as lps
+    >>> import libpysal
 
     Reading data in stl_hom.csv into stl_db to extract values
     for event and population-at-risk variables
 
-    >>> stl_db = lps.open(lps.get_path('stl_hom.csv'),'r')
+    >>> stl_db = libpysal.io.open(libpysal.examples.get_path('stl_hom.csv'),'r')
 
     Reading the stl data in the WKT format so that
     we can easily extract polygon centroids
@@ -1633,7 +1633,7 @@ class Headbanging_Triples(object):
 
     Using the centroids, we create a 5-nearst neighbor weights
 
-    >>> w = lps.knnW_from_array(d,k=5)
+    >>> w = libpysal.weights.KNN(d,k=5)
 
     Ensuring that the elements in the spatial weights instance are ordered
     by the order of stl_db's IDs
@@ -1655,15 +1655,15 @@ class Headbanging_Triples(object):
 
     Opening sids2.shp file
 
-    >>> import libpysal.api as lps
-    >>> sids = lps.open(lps.get_path('sids2.shp'),'r')
+    >>> import libpysal
+    >>> sids = libpysal.io.open(libpysal.examples.get_path('sids2.shp'),'r')
 
     Extracting the centroids of polygons in the sids data
 
     >>> sids_d = np.array([i.centroid for i in sids])
 
     Creating a 5-nearest neighbors weights from the sids centroids
-    >>> sids_w = lps.knnW_from_array(sids_d,k=5)
+    >>> sids_w = libpysal.weights.KNN(sids_d,k=5)
 
     Ensuring that the members in sids_w are ordered by
     the order of sids_d's ID
@@ -1796,11 +1796,11 @@ class Headbanging_Median_Rate(object):
     Examples
     --------
 
-    >>> import libpysal.api as lps
+    >>> import libpysal
 
     opening the sids2 shapefile
 
-    >>> sids = lps.open(lps.get_path('sids2.shp'), 'r')
+    >>> sids = libpysal.io.open(libpysal.examples.get_path('sids2.shp'), 'r')
 
     extracting the centroids of polygons in the sids2 data
 
@@ -1808,7 +1808,7 @@ class Headbanging_Median_Rate(object):
 
     creating a 5-nearest neighbors weights from the centroids
 
-    >>> sids_w = lps.knnW_from_array(sids_d,k=5)
+    >>> sids_w = libpysal.weights.KNN(sids_d,k=5)
 
     ensuring that the members in sids_w are ordered
 
@@ -1823,7 +1823,7 @@ class Headbanging_Median_Rate(object):
 
     reading in the sids2 data table
 
-    >>> sids_db = lps.open(lps.get_path('sids2.dbf'), 'r')
+    >>> sids_db = libpysal.io.open(libpysal.examples.get_path('sids2.dbf'), 'r')
 
     extracting the 10th and 9th columns in the sids2.dbf and
     using data values as event and population-at-risk variables
diff --git a/esda/tests/test_geary.py b/esda/tests/test_geary.py
index 67ea93bf..cfd7bdd1 100644
--- a/esda/tests/test_geary.py
+++ b/esda/tests/test_geary.py
@@ -1,7 +1,7 @@
 """Geary Unittest."""
 import unittest
 
-from libpysal import open as popen
+from libpysal.io import open as popen
 from libpysal import examples
 from libpysal.common import pandas
 
diff --git a/esda/tests/test_mixture_smoothing.py b/esda/tests/test_mixture_smoothing.py
index 5d4c2543..e9806315 100644
--- a/esda/tests/test_mixture_smoothing.py
+++ b/esda/tests/test_mixture_smoothing.py
@@ -1,6 +1,6 @@
 import unittest
 import numpy as np
-import libpysal as pysal
+import libpysal
 from .. import mixture_smoothing as m_s
 
 
diff --git a/esda/tests/test_moran.py b/esda/tests/test_moran.py
index 055c9106..992ec71d 100644
--- a/esda/tests/test_moran.py
+++ b/esda/tests/test_moran.py
@@ -1,5 +1,5 @@
 import unittest
-import libpysal as pysal
+import libpysal
 from libpysal.common import pandas, RTOL, ATOL
 from .. import moran
 import numpy as np
@@ -9,8 +9,8 @@
 
 class Moran_Tester(unittest.TestCase):
     def setUp(self):
-        self.w = pysal.open(pysal.examples.get_path("stl.gal")).read()
-        f = pysal.open(pysal.examples.get_path("stl_hom.txt"))
+        self.w = libpysal.io.open(libpysal.examples.get_path("stl.gal")).read()
+        f = libpysal.io.open(libpysal.examples.get_path("stl_hom.txt"))
         self.y = np.array(f.by_col['HR8893'])
 
     def test_moran(self):
@@ -19,8 +19,8 @@ def test_moran(self):
         self.assertAlmostEqual(mi.p_norm, 0.00013573931385468807)
 
     def test_sids(self):
-        w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
-        f = pysal.open(pysal.examples.get_path("sids2.dbf"))
+        w = libpysal.io.open(libpysal.examples.get_path("sids2.gal")).read()
+        f = libpysal.io.open(libpysal.examples.get_path("sids2.dbf"))
         SIDR = np.array(f.by_col("SIDR74"))
         mi = moran.Moran(SIDR, w, two_tailed=False)
         np.testing.assert_allclose(mi.I, 0.24772519320480135, atol=ATOL, rtol=RTOL)
@@ -28,7 +28,7 @@ def test_sids(self):
 
     def test_variance(self):
         y = np.arange(1, 10)
-        w = pysal.weights.util.lat2W(3, 3)
+        w = libpysal.weights.util.lat2W(3, 3)
         mi = moran.Moran(y, w, transformation='B')
         np.testing.assert_allclose(mi.VI_rand, 0.059687500000000004, atol=ATOL, rtol=RTOL)
         np.testing.assert_allclose(mi.VI_norm, 0.053125000000000006, atol=ATOL, rtol=RTOL)
@@ -44,19 +44,20 @@ def test_z_consistency(self):
     @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
     def test_by_col(self):
         from libpysal.io import geotable as pdio
-        df = pdio.read_files(pysal.examples.get_path('sids2.dbf'))
-        w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
+        np.random.seed(11213)
+        df = pdio.read_files(libpysal.examples.get_path('sids2.dbf'))
+        w = libpysal.io.open(libpysal.examples.get_path("sids2.gal")).read()
         mi = moran.Moran.by_col(df, ['SIDR74'], w=w, two_tailed=False)
-        sidr = np.unique(mi.SIDR74_moran.values)
-        pval = np.unique(mi.SIDR74_p_sim.values)
+        sidr = np.unique(mi.SIDR74_moran.values).item()
+        pval = np.unique(mi.SIDR74_p_sim.values).item()
         np.testing.assert_allclose(sidr, 0.24772519320480135, atol=ATOL, rtol=RTOL)
         self.assertAlmostEqual(pval, 0.001)
 
 
 class Moran_Rate_Tester(unittest.TestCase):
     def setUp(self):
-        self.w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
-        f = pysal.open(pysal.examples.get_path("sids2.dbf"))
+        self.w = libpysal.io.open(libpysal.examples.get_path("sids2.gal")).read()
+        f = libpysal.io.open(libpysal.examples.get_path("sids2.dbf"))
         self.e = np.array(f.by_col['SID79'])
         self.b = np.array(f.by_col['BIR79'])
 
@@ -68,23 +69,24 @@ def test_moran_rate(self):
     @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
     def test_by_col(self):
         from libpysal.io import geotable as pdio
-        df = pdio.read_files(pysal.examples.get_path('sids2.dbf'))
+        np.random.seed(11213)
+        df = pdio.read_files(libpysal.examples.get_path('sids2.dbf'))
         mi = moran.Moran_Rate.by_col(df, ['SID79'], ['BIR79'], w=self.w, two_tailed=False)
-        sidr = np.unique(mi["SID79-BIR79_moran_rate"].values)
-        pval = np.unique(mi["SID79-BIR79_p_sim"].values)
+        sidr = np.unique(mi["SID79-BIR79_moran_rate"].values).item()
+        pval = np.unique(mi["SID79-BIR79_p_sim"].values).item()
         np.testing.assert_allclose(sidr, 0.16622343552567395, rtol=RTOL, atol=ATOL)
-        self.assertAlmostEqual(pval, 0.009)
+        self.assertAlmostEqual(pval, 0.008)
 
 
 
 class Moran_BV_matrix_Tester(unittest.TestCase):
     def setUp(self):
-        f = pysal.open(pysal.examples.get_path("sids2.dbf"))
+        f = libpysal.io.open(libpysal.examples.get_path("sids2.dbf"))
         varnames = ['SIDR74', 'SIDR79', 'NWR74', 'NWR79']
         self.names = varnames
         vars = [np.array(f.by_col[var]) for var in varnames]
         self.vars = vars
-        self.w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
+        self.w = libpysal.io.open(libpysal.examples.get_path("sids2.gal")).read()
 
     def test_Moran_BV_matrix(self):
         res = moran.Moran_BV_matrix(self.vars, self.w, varnames=self.names)
@@ -94,8 +96,8 @@ def test_Moran_BV_matrix(self):
 class Moran_Local_Tester(unittest.TestCase):
     def setUp(self):
         np.random.seed(10)
-        self.w = pysal.open(pysal.examples.get_path("desmith.gal")).read()
-        f = pysal.open(pysal.examples.get_path("desmith.txt"))
+        self.w = libpysal.io.open(libpysal.examples.get_path("desmith.gal")).read()
+        f = libpysal.io.open(libpysal.examples.get_path("desmith.txt"))
         self.y = np.array(f.by_col['z'])
 
     def test_Moran_Local(self):
@@ -117,8 +119,8 @@ def test_by_col(self):
 class Moran_Local_BV_Tester(unittest.TestCase):
     def setUp(self):
         np.random.seed(10)
-        self.w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
-        f = pysal.open(pysal.examples.get_path("sids2.dbf"))
+        self.w = libpysal.io.open(libpysal.examples.get_path("sids2.gal")).read()
+        f = libpysal.io.open(libpysal.examples.get_path("sids2.dbf"))
         self.x = np.array(f.by_col['SIDR79'])
         self.y = np.array(f.by_col['SIDR74'])
 
@@ -132,7 +134,7 @@ def test_Moran_Local_BV(self):
     @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
     def test_by_col(self):
         from libpysal.io import geotable as pdio
-        df = pdio.read_files(pysal.examples.get_path('sids2.dbf'))
+        df = pdio.read_files(libpysal.examples.get_path('sids2.dbf'))
         np.random.seed(12345)
         moran.Moran_Local_BV.by_col(df, ['SIDR74', 'SIDR79'], w=self.w,
                                     inplace=True, outvals=['z_sim', 'p_z_sim'],
@@ -148,8 +150,8 @@ def test_by_col(self):
 class Moran_Local_Rate_Tester(unittest.TestCase):
     def setUp(self):
         np.random.seed(10)
-        self.w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
-        f = pysal.open(pysal.examples.get_path("sids2.dbf"))
+        self.w = libpysal.io.open(libpysal.examples.get_path("sids2.gal")).read()
+        f = libpysal.io.open(libpysal.examples.get_path("sids2.dbf"))
         self.e = np.array(f.by_col['SID79'])
         self.b = np.array(f.by_col['BIR79'])
 
@@ -162,7 +164,7 @@ def test_moran_rate(self):
     @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
     def test_by_col(self):
         from libpysal.io import geotable as pdio
-        df = pdio.read_files(pysal.examples.get_path('sids2.dbf'))
+        df = pdio.read_files(libpysal.examples.get_path('sids2.dbf'))
         lm = moran.Moran_Local_Rate.by_col(df, ['SID79'], ['BIR79'], w=self.w,
                                            outvals=['p_z_sim', 'z_sim'],
                                            transformation='r', permutations=99)
diff --git a/esda/tests/test_smoothing.py b/esda/tests/test_smoothing.py
index 53c54977..ae7a34da 100644
--- a/esda/tests/test_smoothing.py
+++ b/esda/tests/test_smoothing.py
@@ -1,5 +1,5 @@
 import unittest
-import libpysal as pysal
+import libpysal
 from libpysal.weights.Distance import KNN, Kernel
 from .. import smoothing as sm
 import numpy as np
@@ -62,8 +62,8 @@ def test_indirect_age_standardization(self):
 
 class TestSRate(unittest.TestCase):
     def setUp(self):
-        sids = pysal.open(pysal.examples.get_path('sids2.dbf'), 'r')
-        self.w = pysal.open(pysal.examples.get_path('sids2.gal'), 'r').read()
+        sids = libpysal.io.open(libpysal.examples.get_path('sids2.dbf'), 'r')
+        self.w = libpysal.io.open(libpysal.examples.get_path('sids2.gal'), 'r').read()
         self.b, self.e = np.array(sids[:, 8]), np.array(sids[:, 9])
         self.er = [0.453433, 0.000000, 0.775871, 0.973810, 3.133190]
         self.eb = [0.0016973, 0.0017054, 0.0017731, 0.0020129, 0.0035349]
@@ -76,17 +76,17 @@ def setUp(self):
                             3.69333797e-05, 5.40245456e-05, 2.99806055e-05,
                             3.73034109e-05, 3.47270722e-05]).reshape(-1,1)
 
-        self.stl = pysal.open(pysal.examples.get_path('stl_hom.csv'), 'r')
+        self.stl = libpysal.io.open(libpysal.examples.get_path('stl_hom.csv'), 'r')
         self.stl_e, self.stl_b = np.array(self.stl[:, 10]), np.array(self.stl[:, 13])
-        self.stl_w = pysal.open(pysal.examples.get_path('stl.gal'), 'r').read()
+        self.stl_w = libpysal.io.open(libpysal.examples.get_path('stl.gal'), 'r').read()
         if not self.stl_w.id_order_set:
             self.stl_w.id_order = list(range(1, len(self.stl) + 1))
 
         if not PANDAS_EXTINCT:
-            self.df = pysal.open(pysal.examples.get_path('sids2.dbf')).to_df()
+            self.df = libpysal.io.open(libpysal.examples.get_path('sids2.dbf')).to_df()
             self.ename = 'SID74'
             self.bname = 'BIR74'
-            self.stl_df = pysal.open(pysal.examples.get_path('stl_hom.csv')).to_df()
+            self.stl_df = libpysal.io.open(libpysal.examples.get_path('stl_hom.csv')).to_df()
             self.stl_ename = 'HC7984'
             self.stl_bname = 'PO7984'
 
@@ -259,13 +259,13 @@ def test_Smoother_multicol(self):
 
 class TestHB(unittest.TestCase):
     def setUp(self):
-        sids = pysal.open(pysal.examples.get_path('sids2.shp'), 'r')
+        sids = libpysal.io.open(libpysal.examples.get_path('sids2.shp'), 'r')
         self.sids = sids
         self.d = np.array([i.centroid for i in sids])
         self.w = KNN.from_array(self.d, k=5)
         if not self.w.id_order_set:
             self.w.id_order = self.w.id_order
-        sids_db = pysal.open(pysal.examples.get_path('sids2.dbf'), 'r')
+        sids_db = libpysal.io.open(libpysal.examples.get_path('sids2.dbf'), 'r')
         self.b, self.e = np.array(sids_db[:, 8]), np.array(sids_db[:, 9])
         self.sids_hb_rr5 = np.array([0.00075586, 0.,
                                      0.0008285, 0.0018315, 0.00498891])