Accounting for incoming API changes to libpysal and adding testing against libpysal's master branch

.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:
             - [email protected]
             - [email protected]
-        on_change: always
-        on_failure: always
+        on_success: change
+        on_failure: change
 
 after_success:
   - coveralls

esda/gamma.py

@@ -75,7 +75,7 @@ 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)
     >>> y=np.ones(16)

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(liblibpysal.examples.get_path("book.gal")).read()
+    >>> f = libpysal.io.open(liblibpysal.examples.get_path("book.txt"))
     >>> y = np.array(f.by_col['y'])
     >>> c = Geary(y,w,permutations=0)
     >>> round(c.C,7)

esda/getisord.py

@@ -67,7 +67,7 @@ class G(object):
 
     Examples
     --------
-    >>> import libpysal.api as lps
+    >>> import libpysal
     >>> import numpy
     >>> numpy.random.seed(10)
 
@@ -278,7 +278,7 @@ class G_Local(object):
 
     Examples
     --------
-    >>> import libpysal.api as lps
+    >>> import libpysal
     >>> import numpy
     >>> numpy.random.seed(10)
 

esda/join_counts.py

@@ -79,7 +79,7 @@ class Join_Counts(object):
     Replicate example from anselin and rey
 
     >>> import numpy as np
-    >>> import libpysal.api as lps
+    >>> import libpysal
     >>> w = lps.lat2W(4, 4)
     >>> y = np.ones(16)
     >>> y[0:8] = 0

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(liblibpysal.examples.get_path("stl.gal")).read()
+    >>> f = libpysal.io.open(liblibpysal.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(liblibpysal.examples.get_path("sids2.gal")).read()
+    >>> f = libpysal.io.open(liblibpysal.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(liblibpysal.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(liblibpysal.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(liblibpysal.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(liblibpysal.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(liblibpysal.examples.get_path("sids2.gal")).read()
+    >>> f = libpysal.io.open(liblibpysal.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(liblibpysal.examples.get_path("desmith.gal")).read()
+    >>> f = libpysal.io.open(liblibpysal.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(liblibpysal.examples.get_path("sids2.gal")).read()
+    >>> f = libpysal.io.open(liblibpysal.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(liblibpysal.examples.get_path("sids2.gal")).read()
+    >>> f = libpysal.io.open(liblibpysal.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

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(liblibpysal.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(liblibpysal.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(liblibpysal.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(liblibpysal.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(liblibpysal.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(liblibpysal.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(liblibpysal.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(liblibpysal.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(liblibpysal.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(liblibpysal.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(liblibpysal.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(liblibpysal.examples.get_path('stl_hom.csv'),'r')
 
     Reading the stl data in the WKT format so that
     we can easily extract polygon centroids
@@ -1655,8 +1655,8 @@ 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(liblibpysal.examples.get_path('sids2.shp'),'r')
 
     Extracting the centroids of polygons in the sids data
 
@@ -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(liblibpysal.examples.get_path('sids2.shp'), 'r')
 
     extracting the centroids of polygons in the sids2 data
 
@@ -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(liblibpysal.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