add tests and fix comparer for pandas HDF

Author: wkerzendorf

README.rst

@@ -9,7 +9,8 @@ in cases where the arrays are too large to conveniently hard-code them
 in the tests (e.g. ``np.testing.assert_allclose(x, [1, 2, 3])``).
 
 The basic idea is that you can write a test that generates a Numpy array (or
-other related objects depending on the format). You can then either run the
+other related objects depending on the format, e.g. pandas DataFrame).
+You can then either run the
 tests in a mode to **generate** reference files from the arrays, or you can run
 the tests in **comparison** mode, which will compare the results of the tests to
 the reference ones within some tolerance.
@@ -19,6 +20,7 @@ At the moment, the supported file formats for the reference files are:
 -  A plain text-based format (based on Numpy ``loadtxt`` output)
 -  The FITS format (requires `astropy <http://www.astropy.org>`__). With this
    format, tests can return either a Numpy array for a FITS HDU object.
+-  A pandas HDF5 format using the pandas HDFStore
 
 For more information on how to write tests to do this, see the **Using**
 section below.

pytest_arraydiff/plugin.py

@@ -172,10 +172,12 @@ def write(filename, data, **kwargs):
     @classmethod
     def compare(cls, reference_file, test_file, atol=None, rtol=None):
         import pandas.testing as pdt
+        import pandas as pd
 
-
+        ref_data = pd.read_hdf(reference_file)
+        test_data = pd.read_hdf(test_file)
         try:
-            pdt.assert_frame_equal(reference_file, test_file)
+            pdt.assert_frame_equal(ref_data, test_data)
         except AssertionError as exc:
             message = "\n\na: {0}".format(test_file) + '\n'
             message += "b: {0}".format(reference_file) + '\n'

tests/test_pytest_arraydiff.py

@@ -20,6 +20,10 @@ def test_succeeds_func_default():
 def test_succeeds_func_text():
     return np.arange(3 * 5).reshape((3, 5))
 
+@pytest.mark.array_compare(file_format='pdhdf', reference_dir=reference_dir)
+def test_succeeds_func_pdhdf():
+    import pandas as pd
+    return pd.DataFrame(data=np.arange(20), columns=['test_data'])
 
 @pytest.mark.array_compare(file_format='fits', reference_dir=reference_dir)
 def test_succeeds_func_fits():