Upgrading numpy version (#278)

* ENH: upgrade numpy

* DOC: updating numpy docstring

* ENH: upgrading version number

* BLD: setting matplotlib backend to agg

* trimming down ci requirements

* adding back in flake8 and pep8

* adding pip back in

* DOC: raw strings

* flake8

* looks like bokeh flipped the ordering.  weird

* matplotlib tick labels swapped

* py36

* flake8

* forgot import

.travis.yml

@@ -3,10 +3,9 @@
 sudo: false
 language: python
 env:
-  - PYVERSION=3.5 USE_CYTHON=TRUE MAKE_DOC=TRUE
+  - PYVERSION=3.6  USE_CYTHON=TRUE MAKE_DOC=TRUE
 before_install:
-  - "export DISPLAY=:99.0"
-  - "sh -e /etc/init.d/xvfb start"
+  - export MPLBACKEND='Agg'
   - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
   - chmod +x miniconda.sh
   - ./miniconda.sh -b

ci/conda_requirements.txt

@@ -1,10 +1,3 @@
 pip
-nose
-pycodestyle
-flake8
-IPython>4.0.0
-notebook
-scikit-bio=0.5.1
-pyqt=4.11.4
-bokeh
 biom-format
+bokeh=1.1.0

ci/pip_requirements.txt

@@ -1,4 +1,4 @@
 coveralls
-ete3
-statsmodels
 sphinx
+pycodestyle
+flake8

gneiss/__init__.py

@@ -6,4 +6,4 @@
 # The full license is in the file COPYING.txt, distributed with this software.
 # ----------------------------------------------------------------------------
 
-__version__ = "0.4.4"
+__version__ = "0.4.5"

gneiss/composition/_variance.py

@@ -11,7 +11,7 @@
 
 
 def variation_matrix(X):
-    """ Calculate Aitchison variation matrix.
+    r""" Calculate Aitchison variation matrix.
 
     This calculates the Aitchison variation matrix.  Given a compositional
     matrix :math:`X`, and columns :math:`i` and :math:`j`, the :math:`ij` entry

gneiss/plot/tests/test_dendrogram.py

@@ -87,6 +87,7 @@ def test_coords(self):
                            index=['x', 'y', 'child0', 'child1', 'is_tip']).T
 
         res = t.coords(500, 500)
+        exp = exp.loc[res.index]
         pdt.assert_frame_equal(exp, res)
 
     def test_rescale(self):
@@ -148,6 +149,7 @@ def test_coords(self):
                            index=['x', 'y', 'child0', 'child1', 'is_tip']).T
 
         res = t.coords(width=20, height=self.table.shape[0])
+        exp = exp.loc[res.index]
         pdt.assert_frame_equal(exp, res)
 
     def test_rescale(self):

gneiss/plot/tests/test_heatmap.py

@@ -98,13 +98,13 @@ def test_basic(self):
 
         # Make sure that the metadata labels are set properly
         res = str(fig.get_axes()[1].get_xticklabels(minor=True)[0])
-        self.assertEqual(res, "Text(0,0,'a')")
+        self.assertEqual(res, "Text(0, 0, 'a')")
 
         res = str(fig.get_axes()[1].get_xticklabels(minor=True)[1])
-        self.assertEqual(res, "Text(0,0,'b')")
+        self.assertEqual(res, "Text(0, 0, 'b')")
 
         res = str(fig.get_axes()[1].get_xlabel())
-        self.assertEqual(res, "None")
+        self.assertEqual(res, "")
 
     def test_basic_line_width(self):
         fig = heatmap(self.table, self.t, self.md,
@@ -182,17 +182,18 @@ def test_highlights(self):
 
         # Make sure that the metadata labels are set properly
         res = str(fig.get_axes()[2].get_xticklabels(minor=True)[0])
-        self.assertEqual(res, "Text(0,0,'a')")
+        self.assertEqual(res, "Text(0, 0, 'a')")
 
         res = str(fig.get_axes()[2].get_xticklabels(minor=True)[1])
-        self.assertEqual(res, "Text(0,0,'b')")
+        self.assertEqual(res, "Text(0, 0, 'b')")
 
+        print([str(i) for i in fig.get_axes()[1].get_xticklabels()])
         # Make sure that the highlight labels are set properly
         res = str(fig.get_axes()[1].get_xticklabels()[0])
-        self.assertEqual(res, "Text(0,0,'y7')")
+        self.assertEqual(res, "Text(0, 0, 'y8')")
 
         res = str(fig.get_axes()[1].get_xticklabels()[1])
-        self.assertEqual(res, "Text(0,0,'y8')")
+        self.assertEqual(res, "Text(0, 0, 'y7')")
 
         # Test to see if the highlights are ok
         res = fig.get_axes()[2].get_position()._points

gneiss/plot/tests/test_radial.py

@@ -6,6 +6,7 @@
 from skbio import TreeNode, DistanceMatrix
 from gneiss.plot._radial import radialplot
 from gneiss.plot._dendrogram import UnrootedDendrogram
+import numpy.testing as npt
 
 
 class TestRadial(unittest.TestCase):
@@ -49,13 +50,13 @@ def test_basic_plot(self):
                      'hover_var': [None, None, None, None, None],
                      'is_tip': [True, True, True, False, False],
                      'node_size': [10, 10, 10, 10, 10],
-                     'x': [487.5,
-                           12.499999999999972,
+                     'x': [12.499999999999972,
+                           487.5,
                            324.89684138234867,
                            338.26125938385832,
                            193.16888625577729],
-                     'y': [347.7691620070637,
-                           483.28006102610289,
+                     'y': [483.28006102610289,
+                           347.7691620070637,
                            16.719938973897143,
                            271.72822561264161,
                            365.95231443706376]}
@@ -83,9 +84,15 @@ def test_basic_plot(self):
                        node_size='node_size', edge_width='edge_width')
 
         for e in exp_edges.keys():
-            self.assertListEqual(
-                list(p.renderers[0].data_source.data[e]),
-                exp_edges[e])
+            if isinstance(exp_edges[e], float):
+                npt.assert_allclose(
+                    p.renderers[0].data_source.data[e],
+                    np.array(exp_edges[e])
+                )
+            else:
+                self.assertListEqual(
+                    list(p.renderers[0].data_source.data[e]),
+                    exp_edges[e])
 
         for e in exp_nodes.keys():
             self.assertListEqual(

gneiss/regression/tests/test_ols.py

@@ -16,7 +16,7 @@
 from skbio.util import get_data_path
 from gneiss.regression import ols
 from gneiss.balances import balance_basis
-import statsmodels.formula.api as smf
+from statsmodels.regression.linear_model import OLS
 
 
 class TestOLS(unittest.TestCase):
@@ -45,8 +45,8 @@ def setUp(self):
         self.Y = Y
         self.B = B
         self.X = X
-        self.r1_ = smf.OLS(endog=y1, exog=X).fit()
-        self.r2_ = smf.OLS(endog=y2, exog=X).fit()
+        self.r1_ = OLS(endog=y1, exog=X).fit()
+        self.r2_ = OLS(endog=y2, exog=X).fit()
         self.tree = TreeNode.read(['(c, (b,a)y2)y1;'])
 
         self.results = "results"
@@ -257,8 +257,8 @@ def setUp(self):
         self.Y = Y
         self.B = B
         self.X = X
-        self.r1_ = smf.OLS(endog=y1, exog=X).fit()
-        self.r2_ = smf.OLS(endog=y2, exog=X).fit()
+        self.r1_ = OLS(endog=y1, exog=X).fit()
+        self.r2_ = OLS(endog=y2, exog=X).fit()
         self.tree = TreeNode.read(['(c, (b,a)y2)y1;'])
 
     def test_loo(self):

gneiss/sort.py

@@ -148,7 +148,7 @@ def _cache_ntips(tree):
 
 
 def ladderize(tree, ascending=True):
-    """
+    r"""
     Sorts tree according to the size of the subtrees.
 
     Parameters
@@ -200,7 +200,7 @@ def ladderize(tree, ascending=True):
 
 
 def gradient_sort(tree, gradient, ascending=True):
-    """
+    r"""
     Sorts tree according to ordering in gradient.
 
     Parameters

gneiss/util.py

@@ -369,7 +369,8 @@ def _type_cast_to_float(df):
 
 
 def block_diagonal(ncols, nrows, nblocks):
-    """ Generate block diagonal with uniformly distributed values within blocks.
+    """ Generate block diagonal with uniformly distributed values within
+        blocks.
 
     Parameters
     ----------