Remove api.py and account for changes in (incoming) API of mapclassify, esda, and libpysal

.travis.yml

@@ -8,7 +8,7 @@ python:
   - 3.6
 
 env:
-  - PYSAL_PYPI=true
+  - PYSAL_PYPI=true #testing on dependencies libpysal, esda, mapcalssify on pypi
   - PYSAL_PYPI=false
 
 matrix:
@@ -33,8 +33,12 @@ install:
   - pip install -r requirements_tests.txt
   - pip install https://github.com/pysal/splot/archive/master.zip
   - 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 ../;
+        echo 'testing pypi libpysal esda mapclassify' && pip install libpysal esda mapclassify;
+    else
+        echo 'testing git libpysal esda mapclassify';
+        git clone https://github.com/pysal/libpysal.git; cd libpysal; pip install .; cd ../;
+        git clone https://github.com/pysal/mapclassify.git; cd mapclassify; pip install .; cd ../;
+        git clone https://github.com/pysal/esda.git; cd esda; pip install .; cd ../;
     fi;
   # We need the Agg backend to use Matplotlib on TravisCI, otherwise
   # we get an error "DISPLAY variable not set"

LICENSE.txt

@@ -1,4 +1,4 @@
-Copyright 2017, pysal-giddy developers 
+Copyright 2018, pysal-giddy developers 
 
 Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
 

giddy/__init__.py

@@ -12,5 +12,4 @@
 from . import markov
 from . import mobility
 from . import rank
-from . import util
-from . import api
+from . import util

giddy/components.py

@@ -79,8 +79,8 @@ def check_contiguity(w, neighbors, leaver):
 
     Setup imports and a 25x25 spatial weights matrix on a 5x5 square region.
 
-    >>> import libpysal.api as lps
-    >>> w = lps.lat2W(5, 5)
+    >>> import libpysal as lps
+    >>> w = lps.weights.lat2W(5, 5)
 
     Test removing various areas from a subset of the region's areas.  In the
     first case the subset is defined as observations 0, 1, 2, 3 and 4. The

giddy/directional.py

@@ -10,7 +10,7 @@
 import warnings
 
 import numpy as np
-from libpysal.api import lag_spatial
+from libpysal import weights
 
 _POS8 = np.array([1, 1, 0, 0, 1, 1, 0, 0])
 _POS4 = np.array([1, 0, 1, 0])
@@ -93,9 +93,10 @@ def __init__(self, Y, w, k=8):
            Load comma delimited data file in and convert to a numpy array
 
            >>> import libpysal
-           >>> from giddy.api import Rose
+           >>> from giddy.directional import Rose
            >>> import matplotlib.pyplot as plt
-           >>> f=open(libpysal.examples.get_path("spi_download.csv"),'r')
+           >>> file_path = libpysal.examples.get_path("spi_download.csv")
+           >>> f=open(file_path,'r')
            >>> lines=f.readlines()
            >>> f.close()
            >>> lines=[line.strip().split(",") for line in lines]
@@ -132,7 +133,7 @@ def __init__(self, Y, w, k=8):
            Create our contiguity matrix from an external GAL file and row
            standardize the resulting weights
 
-           >>> gal=libpysal.open(libpysal.examples.get_path('states48.gal'))
+           >>> gal=libpysal.io.open(libpysal.examples.get_path('states48.gal'))
            >>> w=gal.read()
            >>> w.transform='r'
 
@@ -299,7 +300,7 @@ def permute(self, permutations=99, alternative='two.sided'):
             print(('Bad option for alternative: %s.' % alternative))
 
     def _calc(self, Y, w, k):
-        wY = lag_spatial(w, Y)
+        wY = weights.lag_spatial(w, Y)
         dx = Y[:, -1] - Y[:,0]
         dy = wY[:, -1] - wY[:, 0]
         self.wY = wY
@@ -343,7 +344,6 @@ def plot(self, attribute=None, ax=None, **kwargs):
 
         """
 
-        use_splot = False
         try:
             import splot.giddy
             use_splot = True
@@ -412,7 +412,7 @@ def plot_vectors(self, arrows=True):
             Axes in which the figure is plotted
 
         """
-        use_splot = False
+
         try:
             import splot.giddy
             use_splot = True

giddy/ergodic.py

@@ -34,7 +34,7 @@ def steady_state(P):
     is melting).
 
     >>> import numpy as np
-    >>> from giddy.api import steady_state
+    >>> from giddy.ergodic import steady_state
     >>> p=np.array([[.5, .25, .25],[.5,0,.5],[.25,.25,.5]])
     >>> steady_state(p)
     array([0.4, 0.2, 0.4])
@@ -79,7 +79,7 @@ def fmpt(P):
     Examples
     --------
     >>> import numpy as np
-    >>> from giddy.api import fmpt
+    >>> from giddy.ergodic import fmpt
     >>> p=np.array([[.5, .25, .25],[.5,0,.5],[.25,.25,.5]])
     >>> fm=fmpt(p)
     >>> fm
@@ -137,7 +137,7 @@ def var_fmpt(P):
     Examples
     --------
     >>> import numpy as np
-    >>> from giddy.api import var_fmpt
+    >>> from giddy.ergodic import var_fmpt
     >>> p=np.array([[.5, .25, .25],[.5,0,.5],[.25,.25,.5]])
     >>> vfm=var_fmpt(p)
     >>> vfm

giddy/markov.py

@@ -12,9 +12,9 @@
 from .components import Graph
 from scipy import stats
 from operator import gt
-import libpysal.api as ps
+from libpysal import weights
 from esda.moran import Moran_Local
-import mapclassify.api as mc
+import mapclassify as mc
 import itertools
 
 # TT predefine LISA transitions
@@ -75,7 +75,7 @@ class Markov(object):
     Examples
     --------
     >>> import numpy as np
-    >>> from giddy.api import Markov
+    >>> from giddy.markov import Markov
     >>> c = [['b','a','c'],['c','c','a'],['c','b','c']]
     >>> c.extend([['a','a','b'], ['a','b','c']])
     >>> c = np.array(c)
@@ -92,8 +92,8 @@ class Markov(object):
     US nominal per capita income 48 states 81 years 1929-2009
 
     >>> import libpysal
-    >>> import mapclassify.api as mc
-    >>> f = libpysal.open(libpysal.examples.get_path("usjoin.csv"))
+    >>> import mapclassify as mc
+    >>> f = libpysal.io.open(libpysal.examples.get_path("usjoin.csv"))
     >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)])
 
     set classes to quintiles for each year
@@ -303,13 +303,13 @@ class Spatial_Markov(object):
     Examples
     --------
     >>> import libpysal
-    >>> from giddy.api import Spatial_Markov
+    >>> from giddy.markov import Spatial_Markov
     >>> import numpy as np
-    >>> f = libpysal.open(libpysal.examples.get_path("usjoin.csv"))
+    >>> f = libpysal.io.open(libpysal.examples.get_path("usjoin.csv"))
     >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)])
     >>> pci = pci.transpose()
     >>> rpci = pci/(pci.mean(axis=0))
-    >>> w = libpysal.open(libpysal.examples.get_path("states48.gal")).read()
+    >>> w = libpysal.io.open(libpysal.examples.get_path("states48.gal")).read()
     >>> w.transform = 'r'
 
     Now we create a `Spatial_Markov` instance for the continuous relative per
@@ -550,7 +550,7 @@ class Spatial_Markov(object):
     Let's still use the US state income time series to demonstrate. We first
     discretize them into categories and then pass them to Spatial_Markov.
 
-    >>> import mapclassify.api as mc
+    >>> import mapclassify as mc
     >>> y = mc.Quantiles(rpci.flatten(), k=5).yb.reshape(rpci.shape)
     >>> np.random.seed(5)
     >>> sm = Spatial_Markov(y, w, discrete=True, variable_name='discretized rpci')
@@ -738,10 +738,10 @@ def _calc(self, y, w):
         '''
         if self.discrete:
             #np.random.seed(24788)
-            self.lclass_ids = ps.lag_categorical(w, self.class_ids,
+            self.lclass_ids = weights.lag_categorical(w, self.class_ids,
                                                  ties="tryself")
         else:
-            ly = ps.lag_spatial(w, y)
+            ly = weights.lag_spatial(w, y)
             self.lclass_ids, self.lag_cutoffs,self.m = self._maybe_classify(
                 ly, self.m, self.lag_cutoffs)
             self.lclasses = np.arange(self.m)
@@ -881,13 +881,12 @@ def chi2(T1, T2):
     Examples
     --------
     >>> import libpysal
-    >>> from giddy.api import Spatial_Markov
-    >>> from giddy.markov import chi2
-    >>> f = libpysal.open(libpysal.examples.get_path("usjoin.csv"))
+    >>> from giddy.markov import Spatial_Markov, chi2
+    >>> f = libpysal.io.open(libpysal.examples.get_path("usjoin.csv"))
     >>> years = list(range(1929, 2010))
     >>> pci = np.array([f.by_col[str(y)] for y in years]).transpose()
     >>> rpci = pci/(pci.mean(axis=0))
-    >>> w = libpysal.open(libpysal.examples.get_path("states48.gal")).read()
+    >>> w = libpysal.io.open(libpysal.examples.get_path("states48.gal")).read()
     >>> w.transform='r'
     >>> sm = Spatial_Markov(rpci, w, fixed=True)
     >>> T1 = sm.T[0]
@@ -1078,11 +1077,11 @@ class LISA_Markov(Markov):
     --------
     >>> import libpysal
     >>> import numpy as np
-    >>> from giddy.api import LISA_Markov
-    >>> f = libpysal.open(libpysal.examples.get_path("usjoin.csv"))
+    >>> from giddy.markov import LISA_Markov
+    >>> f = libpysal.io.open(libpysal.examples.get_path("usjoin.csv"))
     >>> years = list(range(1929, 2010))
     >>> pci = np.array([f.by_col[str(y)] for y in years]).transpose()
-    >>> w = libpysal.open(libpysal.examples.get_path("states48.gal")).read()
+    >>> w = libpysal.io.open(libpysal.examples.get_path("states48.gal")).read()
     >>> lm = LISA_Markov(pci,w)
     >>> lm.classes
     array([1, 2, 3, 4])
@@ -1194,7 +1193,7 @@ def __init__(self, y, w, permutations=0,
 
         ybar = y.mean(axis=0)
         r = y / ybar
-        ylag = np.array([ps.lag_spatial(w, yt) for yt in y])
+        ylag = np.array([weights.lag_spatial(w, yt) for yt in y])
         rlag = ylag / ybar
         rc = r < 1.
         rlagc = rlag < 1.
@@ -1244,11 +1243,11 @@ def spillover(self, quadrant=1, neighbors_on=False):
         Examples
         --------
         >>> import libpysal
-        >>> from giddy.api import LISA_Markov
-        >>> f = libpysal.open(libpysal.examples.get_path("usjoin.csv"))
+        >>> from giddy.markov import LISA_Markov
+        >>> f = libpysal.io.open(libpysal.examples.get_path("usjoin.csv"))
         >>> years = list(range(1929, 2010))
         >>> pci = np.array([f.by_col[str(y)] for y in years]).transpose()
-        >>> w = libpysal.open(libpysal.examples.get_path("states48.gal")).read()
+        >>> w = libpysal.io.open(libpysal.examples.get_path("states48.gal")).read()
         >>> np.random.seed(10)
         >>> lm_random = LISA_Markov(pci, w, permutations=99)
         >>> r = lm_random.spillover()
@@ -1383,7 +1382,7 @@ def kullback(F):
     Examples
     --------
     >>> import numpy as np
-    >>> from giddy.api import kullback
+    >>> from giddy.markov import kullback
     >>> s1 = np.array([
     ...         [ 22, 11, 24,  2,  2,  7],
     ...         [ 5, 23, 15,  3, 42,  6],
@@ -1469,8 +1468,8 @@ def prais(pmat):
     --------
     >>> import numpy as np
     >>> import libpysal
-    >>> from giddy.api import prais
-    >>> f = libpysal.open(libpysal.examples.get_path("usjoin.csv"))
+    >>> from giddy.markov import Markov,prais
+    >>> f = libpysal.io.open(libpysal.examples.get_path("usjoin.csv"))
     >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)])
     >>> q5 = np.array([mc.Quantiles(y).yb for y in pci]).transpose()
     >>> m = Markov(q5)

giddy/mobility.py

@@ -53,9 +53,10 @@ def markov_mobility(p, measure="P",ini=None):
     --------
     >>> import numpy as np
     >>> import libpysal
-    >>> import mapclassify.api as mc
-    >>> from giddy.api import Markov, markov_mobility
-    >>> f = libpysal.open(libpysal.examples.get_path("usjoin.csv"))
+    >>> import mapclassify as mc
+    >>> from giddy.markov import Markov
+    >>> from giddy.mobility import markov_mobility
+    >>> f = libpysal.io.open(libpysal.examples.get_path("usjoin.csv"))
     >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)])
     >>> q5 = np.array([mc.Quantiles(y).yb for y in pci]).transpose()
     >>> m = Markov(q5)
@@ -68,32 +69,37 @@ def markov_mobility(p, measure="P",ini=None):
 
     (1) Estimate Shorrock1 mobility index:
 
-    >>> markov_mobility(m.p, measure="P")
-    0.19758992000997844
+    >>> mobi_1 = markov_mobility(m.p, measure="P")
+    >>> print("{:.5f}".format(mobi_1))
+    0.19759
 
     (2) Estimate Shorrock2 mobility index:
 
-    >>> markov_mobility(m.p, measure="D")
-    0.6068485462369559
+    >>> mobi_2 = markov_mobility(m.p, measure="D")
+    >>> print("{:.5f}".format(mobi_2))
+    0.60685
 
     (3) Estimate Sommers and Conlisk mobility index:
 
-    >>> markov_mobility(m.p, measure="L2")
-    0.03978200230815965
+    >>> mobi_3 = markov_mobility(m.p, measure="L2")
+    >>> print("{:.5f}".format(mobi_3))
+    0.03978
 
     (4) Estimate Bartholomew1 mobility index (note that the initial
     distribution should be given):
 
     >>> ini = np.array([0.1,0.2,0.2,0.4,0.1])
-    >>> markov_mobility(m.p, measure = "B1", ini=ini)
-    0.2277675878319787
+    >>> mobi_4 = markov_mobility(m.p, measure = "B1", ini=ini)
+    >>> print("{:.5f}".format(mobi_4))
+    0.22777
 
     (5) Estimate Bartholomew2 mobility index (note that the initial
     distribution should be given):
 
     >>> ini = np.array([0.1,0.2,0.2,0.4,0.1])
-    >>> markov_mobility(m.p, measure = "B2", ini=ini)
-    0.04636660119478926
+    >>> mobi_5 = markov_mobility(m.p, measure = "B2", ini=ini)
+    >>> print("{:.5f}".format(mobi_5))
+    0.04637
 
     """