add tests for improved coverage

pydl/pcomp.py

@@ -13,7 +13,7 @@ class pcomp(object):
     Parameters
     ----------
     x : array-like
-        A 2-D array with N rows and M columns.
+        A 2-D array with :math:`N` rows and :math:`M` columns.
     standardize : :class:`bool`, optional
         If set to ``True``, the input data will have its mean subtracted off
         and will be scaled to unit variance.
@@ -79,22 +79,23 @@ def coefficients(self):
 
     @lazyproperty
     def derived(self):
-        """(:class:`~numpy.ndarray`) A :math:`N \times M` array containing
-        the derived variables."""
+        """(:class:`~numpy.ndarray`) The derived variables.
+        """
         derived_data = np.dot(self._array, self.coefficients)
         if self._standardize:
             derived_data += self._xstd
         return derived_data
 
     @lazyproperty
     def variance(self):
-        """(:class:`~numpy.ndarray`) An array of the :math:`M` variances of
-        each derived variable."""
+        """(:class:`~numpy.ndarray`) The variances of each derived variable.
+        """
         return self._evals/self._c.trace()
 
     @lazyproperty
     def eigenvalues(self):
-        """(:class:`~numpy.ndarray`) An array of the :math:`M` eigenvalues
-        that correspond to the principal components.
+        """(:class:`~numpy.ndarray`) The eigenvalues.
+        There is one eigenvalue for that correspond each the principal
+        component.
         """
         return self._evals

pydl/pydlspec2d/spec1d/spec_append.py

@@ -14,7 +14,7 @@ def spec_append(spec1, spec2, pixshift=0):
         Append `spec2` to `spec1`.
     pixshift : :class:`int`, optional
         If `pixshift` is set to a positive integer, `spec2` will be padded with
-        `pixshift` zeros on the left size.  If `pixshift` is set to a
+        `pixshift` zeros on the left side.  If `pixshift` is set to a
         negative integer, `spec1` will be padded with ``abs(pixshift)`` zeros
         on the left side.  If not set, all zeros will be padded on the right
         side.

pydl/pydlspec2d/spec1d/tests/test_spec_append.py

@@ -0,0 +1,38 @@
+# Licensed under a 3-clause BSD style license - see LICENSE.rst
+# -*- coding: utf-8 -*-
+
+
+def test_spec_append():
+    import os
+    from numpy import array
+    from .. import spec_append
+    spec1 = array([[1, 1, 1, 1, 1],
+                   [1, 1, 1, 1, 1]])
+    spec2 = array([[2, 2, 2, 2, 2],
+                   [2, 2, 2, 2, 2]])
+    s = spec_append(spec1, spec2)
+    assert (s == array([[1, 1, 1, 1, 1],
+                        [1, 1, 1, 1, 1],
+                        [2, 2, 2, 2, 2],
+                        [2, 2, 2, 2, 2]])).all()
+    spec2 = array([[2, 2, 2, 2],
+                   [2, 2, 2, 2]])
+    s = spec_append(spec1, spec2)
+    assert (s == array([[1, 1, 1, 1, 1],
+                        [1, 1, 1, 1, 1],
+                        [2, 2, 2, 2, 0],
+                        [2, 2, 2, 2, 0]])).all()
+    s = spec_append(spec1, spec2, 1)
+    assert (s == array([[1, 1, 1, 1, 1],
+                        [1, 1, 1, 1, 1],
+                        [0, 2, 2, 2, 2],
+                        [0, 2, 2, 2, 2]])).all()
+    spec1 = array([[1, 1, 1],
+                   [1, 1, 1]])
+    spec2 = array([[2, 2, 2, 2, 2],
+                   [2, 2, 2, 2, 2]])
+    s = spec_append(spec1, spec2, -2)
+    assert (s == array([[0, 0, 1, 1, 1],
+                        [0, 0, 1, 1, 1],
+                        [2, 2, 2, 2, 2],
+                        [2, 2, 2, 2, 2]])).all()

pydl/pydlspec2d/spec1d/tests/test_spec_path.py

@@ -0,0 +1,35 @@
+# Licensed under a 3-clause BSD style license - see LICENSE.rst
+# -*- coding: utf-8 -*-
+
+
+def test_spec_path():
+    import os
+    from numpy import array
+    from .. import spec_path
+    if 'BOSS_SPECTRO_REDUX' in os.environ:
+        bsr_orig = os.environ['BOSS_SPECTRO_REDUX']
+        bsr = bsr_orig
+    else:
+        bsr_orig = None
+        bsr = '/boss/spectro/redux'
+        os.environ['BOSS_SPECTRO_REDUX'] = bsr
+    if 'RUN2D' in os.environ:
+        run2d_orig = os.environ['RUN2D']
+        run2d = run2d_orig
+    else:
+        run2d_orig = None
+        run2d = 'v1_2_3'
+        os.environ['RUN2D'] = run2d
+    p = spec_path(1234)
+    assert p[0] == os.path.join(bsr, run2d, '1234')
+    p = spec_path(1234, topdir=bsr, run2d=run2d)
+    assert p[0] == os.path.join(bsr, run2d, '1234')
+    p = spec_path(array([1234, 5678]), topdir=bsr, run2d=run2d)
+    assert p[0] == os.path.join(bsr, run2d, '1234')
+    assert p[1] == os.path.join(bsr, run2d, '5678')
+    p = spec_path(1234, path=bsr)
+    assert p[0] == bsr
+    if bsr_orig is None:
+        del os.environ['BOSS_SPECTRO_REDUX']
+    if run2d_orig is None:
+        del os.environ['RUN2D']

pydl/tests/t/pcomp_data.txt

@@ -50,3 +50,7 @@
 73.4205, 14.7100, 3.86270, 0.0159930
 # variance
 0.797969, 0.159875, 0.0419817, 0.000173819
+# eigenvalues of standardized data
+2.99966981, 1.05397516, 1.56189601e-01, 6.91743392e-04
+# variance of standardized data
+7.12421581e-01, 2.50319100e-01, 3.70950302e-02, 1.64289056e-04

pydl/tests/test_pydl.py

@@ -81,7 +81,7 @@ def test_pcomp(self):
         pcomp_data = test_data[0:20, :]
         m = 4
         n = 20
-        means = np.tile(pcomp_data.mean(0), 20).reshape(pcomp_data.shape)
+        means = np.tile(pcomp_data.mean(0), n).reshape(pcomp_data.shape)
         newarray = pcomp_data - means
         foo = pcomp(newarray, covariance=True)
         #
@@ -101,6 +101,20 @@ def test_pcomp(self):
         assert_allclose(foo.eigenvalues, eigenvalues, 1e-4)
         variance = test_data[45, :]
         assert_allclose(foo.variance, variance, 1e-4)
+        #
+        # Test the standardization.
+        #
+        foo = pcomp(pcomp_data, standardize=True, covariance=True)
+        # for k in range(m):
+        #     assert_allclose(abs(foo.derived[:, k]), abs(derived[:, k]), 1e-4)
+        # for k in range(m):
+        #     assert_allclose(abs(foo.coefficients[:, k]),
+        #                     abs(coefficientsT[:, k]),
+        #                     1e-4)
+        eigenvalues = test_data[46, :]
+        assert_allclose(foo.eigenvalues, eigenvalues, 1e-4)
+        variance = test_data[47, :]
+        assert_allclose(foo.variance, variance, 1e-4)
 
     def test_smooth(self):
         test_data_file = join(self.data_dir, 'smooth_data.txt')