Changed comments to docstrings where necessary in theano/tests

theano/tests/test_flake8.py

@@ -107,8 +107,9 @@ whitelist_flake8 = [
 
 
 def list_files(dir_path=theano.__path__[0], pattern='*.py', no_match=".#"):
-    # List all files under theano's path.
-
+    """
+    List all files under theano's path.
+    """
     files_list = []
     for (dir, _, files) in os.walk(dir_path):
         for f in files:
@@ -121,7 +122,6 @@ def list_files(dir_path=theano.__path__[0], pattern='*.py', no_match=".#"):
 
 def test_format_flake8():
     # Test if flake8 is respected.
-
     if not flake8_available:
         raise SkipTest("flake8 is not installed")
     total_errors = 0
@@ -137,10 +137,11 @@ def test_format_flake8():
 
 
 def print_files_information_flake8():
-    # Print the list of files which can be removed from the whitelist and the
-    # list of files which do not respect FLAKE8 formatting that aren't in the
-    # whitelist.
-
+    """
+    Print the list of files which can be removed from the whitelist and the
+    list of files which do not respect FLAKE8 formatting that aren't in the
+    whitelist.
+    """
     infracting_files = []
     non_infracting_files = []
     for path in list_files():
@@ -162,10 +163,12 @@ def print_files_information_flake8():
 
 
 def check_all_files(dir_path=theano.__path__[0], pattern='*.py'):
-    # List all .py files under dir_path (theano path), check if they follow
-    # flake8 format, save all the error-formatted files into
-    # theano_filelist.txt. This function is used for generating
-    # the "whitelist_flake8" in this file.
+    """
+    List all .py files under dir_path (theano path), check if they follow
+    flake8 format, save all the error-formatted files into
+    theano_filelist.txt. This function is used for generating
+    the "whitelist_flake8" in this file.
+    """
 
     with open('theano_filelist.txt', 'a') as f_txt:
         for (dir, _, files) in os.walk(dir_path):

theano/tests/test_gradient.py

@@ -21,9 +21,10 @@ one = theano.tensor.as_tensor_variable(1.)
 
 
 def grad_sources_inputs(sources, inputs):
-    # This implements the old grad_sources_inputs function in terms of
-    # the new interface so the tests don't need to be rewritten.
-
+    """
+    This implements the old grad_sources_inputs function in terms of
+    the new interface so the tests don't need to be rewritten.
+    """
     if inputs is None:
         inputs = theano.gof.graph.inputs([source[0] for source in sources])
     return dict(izip(inputs, theano.gradient.grad(cost=None, known_grads=dict(sources),

theano/tests/test_rop.py

 """
- WRITE ME
+WRITE ME
 
- Tests for the R operator / L operator
+Tests for the R operator / L operator
 
- For the list of op with r op defined, with or without missing test
- see this file: doc/library/tensor/basic.txt
-
- For function to automatically test your Rop implementation, look at
- the docstring of the functions: check_mat_rop_lop, check_rop_lop,
- check_nondiff_rop,
+For the list of op with r op defined, with or without missing test
+see this file: doc/library/tensor/basic.txt
 
+For function to automatically test your Rop implementation, look at
+the docstring of the functions: check_mat_rop_lop, check_rop_lop,
+check_nondiff_rop,
 """
 from __future__ import absolute_import, print_function, division
 import unittest
@@ -30,9 +29,10 @@ Special Op created to test what happens when you have one op that is not
 differentiable in the computational graph
 '''
 
-
 class BreakRop(Op):
-    # @note: Non-differentiable.
+    """
+    @note: Non-differentiable.
+    """
     __props__ = ()
 
     def make_node(self, x):
@@ -53,9 +53,10 @@ break_op = BreakRop()
 
 
 class RopLop_checker(unittest.TestCase):
-    # Don't peform any test, but provide the function to test the
-    # Rop to class that inherit from it.
-
+    """
+    Don't peform any test, but provide the function to test the
+    Rop to class that inherit from it.
+    """
     def setUp(self):
         utt.seed_rng()
         # Using vectors make things a lot simpler for generating the same
@@ -70,8 +71,10 @@ class RopLop_checker(unittest.TestCase):
                              5 + self.rng.randint(3))
 
     def check_nondiff_rop(self, y):
-        # If your op is not differentiable(so you can't define Rop)
-        # test that an error is raised.
+        """
+        If your op is not differentiable(so you can't define Rop)
+        test that an error is raised.
+        """
         raised = False
         try:
             tensor.Rop(y, self.x, self.v)
@@ -83,24 +86,25 @@ class RopLop_checker(unittest.TestCase):
                 ' is not differentiable'))
 
     def check_mat_rop_lop(self, y, out_shape):
-        # Test the Rop/Lop when input is a matrix and the output is a vector
-        #
-        # :param y: the output variable of the op applied to self.mx
-        # :param out_shape: Used to generate a random tensor
-        #                   corresponding to the evaluation point of the Rop
-        #                   (i.e. the tensor with which you multiply the
-        #                   Jacobian). It should be a tuple of ints.
-        #
-        # If the Op has more than 1 input, one of them must be mx, while
-        # others must be shared variables / constants. We will test only
-        # against the input self.mx, so you must call
-        # check_mat_rop_lop/check_rop_lop for the other inputs.
-        #
-        # We expect all inputs/outputs have dtype floatX.
-        #
-        # If you want to test an Op with an output matrix, add a sum
-        # after the Op you want to test.
+        """
+        Test the Rop/Lop when input is a matrix and the output is a vector
+
+        :param y: the output variable of the op applied to self.mx
+        :param out_shape: Used to generate a random tensor
+                          corresponding to the evaluation point of the Rop
+                          (i.e. the tensor with which you multiply the
+                          Jacobian). It should be a tuple of ints.
 
+        If the Op has more than 1 input, one of them must be mx, while
+        others must be shared variables / constants. We will test only
+        against the input self.mx, so you must call
+        check_mat_rop_lop/check_rop_lop for the other inputs.
+
+        We expect all inputs/outputs have dtype floatX.
+
+        If you want to test an Op with an output matrix, add a sum
+        after the Op you want to test.
+        """
         vx = np.asarray(self.rng.uniform(size=self.mat_in_shape),
                         theano.config.floatX)
         vv = np.asarray(self.rng.uniform(size=self.mat_in_shape),
@@ -132,9 +136,10 @@ class RopLop_checker(unittest.TestCase):
         assert np.allclose(v1, v2), ('LOP mismatch: %s %s' % (v1, v2))
 
     def check_rop_lop(self, y, out_shape):
-        # As check_mat_rop_lop, except the input is self.x which is a
-        # vector. The output is still a vector.
-
+        """
+        As check_mat_rop_lop, except the input is self.x which is a
+        vector. The output is still a vector.
+        """
         # TEST ROP
         vx = np.asarray(self.rng.uniform(size=self.in_shape),
                         theano.config.floatX)