MAINT: Add odl and np to default namespace for doctests, closes #410

conftest.py

@@ -23,14 +23,31 @@
 
 import pytest
 import odl
+import numpy as np
 from odl.trafos.wavelet import PYWAVELETS_AVAILABLE
 
+
+# --- Add numpy and ODL to all doctests ---
+
+
+@pytest.fixture(autouse=True)
+def add_np(doctest_namespace):
+    doctest_namespace['np'] = np
+    doctest_namespace['odl'] = odl
+
+
+# --- Files to be ignored by the tests ---
+
+
 collect_ignore = ['setup.py', 'run_tests.py']
 
 if not PYWAVELETS_AVAILABLE:
     collect_ignore.append('odl/trafos/wavelet.py')
 
 
+# --- Options flags for the tests ---
+
+
 def pytest_addoption(parser):
     parser.addoption('--largescale', action='store_true',
                      help='Run large and slow tests')
@@ -42,21 +59,25 @@ def pytest_addoption(parser):
                      help='Run examples')
 
 
-# reusable fixtures
+# --- Reusable fixtures ---
+
 fn_impl_params = odl.FN_IMPLS.keys()
 fn_impl_ids = [" impl='{}' ".format(p) for p in fn_impl_params]
 
 
 @pytest.fixture(scope="module", ids=fn_impl_ids, params=fn_impl_params)
 def fn_impl(request):
+    """String with a available `FnBase` implementation name."""
     return request.param
 
 ntuples_impl_params = odl.NTUPLES_IMPLS.keys()
 ntuples_impl_ids = [" impl='{}' ".format(p) for p in ntuples_impl_params]
 
 
-@pytest.fixture(scope="module", ids=ntuples_impl_ids, params=ntuples_impl_params)
+@pytest.fixture(scope="module", ids=ntuples_impl_ids,
+                params=ntuples_impl_params)
 def ntuples_impl(request):
+    """String with a available `NtuplesBase` implementation name."""
     return request.param
 
 ufunc_params = [ufunc for ufunc in odl.util.ufuncs.UFUNCS]
@@ -65,6 +86,19 @@ def ntuples_impl(request):
 
 @pytest.fixture(scope="module", ids=ufunc_ids, params=ufunc_params)
 def ufunc(request):
+    """Tuple with information on a ufunc.
+
+    Returns
+    -------
+    name : `str`
+        Name of the ufunc.
+    n_in : `int`
+        Number of input values of the ufunc.
+    n_out : `int`
+        Number of output values of the ufunc.
+    doc : `str`
+        Docstring for the ufunc.
+    """
     return request.param
 
 
@@ -74,4 +108,13 @@ def ufunc(request):
 
 @pytest.fixture(scope="module", ids=reduction_ids, params=reduction_params)
 def reduction(request):
+    """Tuple with information on a reduction.
+
+    Returns
+    -------
+    name : `str`
+        Name of the reduction.
+    doc : `str`
+        Docstring for the reduction.
+    """
     return request.param

doc/source/dev/document.rst

@@ -86,6 +86,7 @@ Some short tips
   ```numpy.ndarray```).
 * Make sure that the first line is short and descriptive.
 * Examples are often better than long descriptions.
+* Numpy and ODL are both imported by default in doctests, so there is no need for ``import numpy as np`` or ``import odl``.
 
 Quick summary of `PEP257`_
 --------------------------

odl/deform/linearized.py

@@ -66,7 +66,6 @@ def _linear_deform(template, displacement, out=None):
     In the 4-th point, the value is taken from 0.2 (one cell) to the
     left, i.e. 1.0.
 
-    >>> import odl
     >>> space = odl.uniform_discr(0, 1, 5)
     >>> disp_field_space = space.vector_field_space
     >>> template = space.element([0, 0, 1, 0, 0])
@@ -153,7 +152,6 @@ def __init__(self, template):
         In the 4-th point, the value is taken from 0.2 (one cell) to the
         left, i.e. 1.0.
 
-        >>> import odl
         >>> space = odl.uniform_discr(0, 1, 5, interp='nearest')
         >>> template = space.element([0, 0, 1, 0, 0])
         >>> op = LinDeformFixedTempl(template)
@@ -282,7 +280,6 @@ def __init__(self, displacement, templ_space=None):
         In the 4-th point, the value is taken from 0.2 (one cell) to the
         left, i.e. 1.0.
 
-        >>> import odl
         >>> space = odl.uniform_discr(0, 1, 5)
         >>> disp_field = space.vector_field_space.element([[0, 0, 0, -0.2, 0]])
         >>> op = LinDeformFixedDisp(disp_field)

odl/diagnostics/examples.py

@@ -41,7 +41,6 @@ def samples(*sets):
 
     Examples
     --------
-    >>> import odl
     >>> R, C = odl.RealNumbers(), odl.ComplexNumbers()
     >>> for [name_x, x], [name_y, y] in samples(R, C): pass  # use examples
     """

odl/discr/diff_ops.py

@@ -118,7 +118,6 @@ def _call(self, x, out=None):
 
         Examples
         --------
-        >>> import odl
         >>> data = np.array([[ 0.,  1.,  2.,  3.,  4.],
         ...                  [ 0.,  2.,  4.,  6.,  8.]])
         >>> discr = odl.uniform_discr([0, 0], [2, 1], data.shape)
@@ -219,7 +218,6 @@ def __init__(self, domain=None, range=None, method='forward',
 
         Examples
         --------
-        >>> import odl
         >>> dom = odl.uniform_discr([0, 0], [1, 1], (10, 20))
         >>> ran = odl.ProductSpace(dom, dom.ndim)  # 2-dimensional
         >>> grad_op = Gradient(dom)
@@ -273,10 +271,9 @@ def _call(self, x, out=None):
 
         Examples
         --------
-        >>> from odl import uniform_discr
         >>> data = np.array([[ 0., 1., 2., 3., 4.],
         ...                  [ 0., 2., 4., 6., 8.]])
-        >>> discr = uniform_discr([0, 0], [2, 5], data.shape)
+        >>> discr = odl.uniform_discr([0, 0], [2, 5], data.shape)
         >>> f = discr.element(data)
         >>> grad = Gradient(discr)
         >>> grad_f = grad(f)
@@ -400,7 +397,6 @@ def __init__(self, domain=None, range=None, method='forward',
 
         Examples
         --------
-        >>> import odl
         >>> ran = odl.uniform_discr([0, 0], [1, 1], (10, 20))
         >>> dom = odl.ProductSpace(ran, ran.ndim)  # 2-dimensional
         >>> div_op = Divergence(dom)
@@ -455,11 +451,10 @@ def _call(self, x, out=None):
 
         Examples
         --------
-        >>> from odl import uniform_discr
         >>> data = np.array([[0., 1., 2., 3., 4.],
         ...                  [1., 2., 3., 4., 5.],
         ...                  [2., 3., 4., 5., 6.]])
-        >>> space = uniform_discr([0, 0], [3, 5], data.shape)
+        >>> space = odl.uniform_discr([0, 0], [3, 5], data.shape)
         >>> div = Divergence(range=space)
         >>> f = div.domain.element([data, data])
         >>> div_f = div(f)
@@ -591,11 +586,10 @@ def _call(self, x, out=None):
 
         Examples
         --------
-        >>> from odl import uniform_discr
         >>> data = np.array([[ 0., 0., 0.],
         ...                  [ 0., 1., 0.],
         ...                  [ 0., 0., 0.]])
-        >>> space = uniform_discr([0, 0], [3, 3], data.shape)
+        >>> space = odl.uniform_discr([0, 0], [3, 3], data.shape)
         >>> f = space.element(data)
         >>> lap = Laplacian(space)
         >>> print(lap(f))

odl/discr/discr_mappings.py

@@ -236,13 +236,11 @@ def _call(self, func, out=None, **kwargs):
         Define a set of functions from the rectangle [1, 3] x [2, 5]
         to the real numbers:
 
-        >>> from odl import FunctionSpace, Rectangle
-        >>> rect = Rectangle([1, 3], [2, 5])
-        >>> funcset = FunctionSpace(rect)
+        >>> rect = odl.Rectangle([1, 3], [2, 5])
+        >>> funcset = odl.FunctionSpace(rect)
 
         Partition the rectangle by a tensor grid:
 
-        >>> import odl
         >>> rect = odl.Rectangle([1, 3], [2, 5])
         >>> grid = odl.TensorGrid([1, 2], [3, 4, 5])
         >>> partition = odl.RectPartition(rect, grid)
@@ -398,22 +396,19 @@ def _call(self, x, out=None):
         We test nearest neighbor interpolation with a non-scalar
         data type in 2d:
 
-        >>> import numpy as np
-        >>> from odl import Rectangle, Strings, FunctionSet
-        >>> rect = Rectangle([0, 0], [1, 1])
-        >>> strings = Strings(1)  # 1-char strings
-        >>> space = FunctionSet(rect, strings)
+        >>> rect = odl.Rectangle([0, 0], [1, 1])
+        >>> strings = odl.Strings(1)  # 1-char strings
+        >>> space = odl.FunctionSet(rect, strings)
 
         Partitioning the domain uniformly with no nodes on the boundary
         (will shift the grid points):
 
-        >>> from odl import uniform_partition_fromintv, ntuples
-        >>> part = uniform_partition_fromintv(rect, [4, 2],
-        ...                                   nodes_on_bdry=False)
+        >>> part = odl.uniform_partition_fromintv(rect, [4, 2],
+        ...                                       nodes_on_bdry=False)
         >>> part.grid.coord_vectors
         (array([ 0.125,  0.375,  0.625,  0.875]), array([ 0.25,  0.75]))
 
-        >>> dspace = ntuples(part.size, dtype='U1')
+        >>> dspace = odl.ntuples(part.size, dtype='U1')
 
         Now we initialize the operator and test it with some points:
 

odl/discr/discr_ops.py

@@ -56,7 +56,6 @@ def __init__(self, domain, range):
         Create two spaces with different number of points and a resampling
         operator.
 
-        >>> import odl
         >>> X = odl.uniform_discr(0, 1, 3)
         >>> Y = odl.uniform_discr(0, 1, 6)
         >>> resampling = Resampling(X, Y)
@@ -79,7 +78,6 @@ def _call(self, x, out=None):
         Create two spaces with different number of points and create resampling
         operator. Apply operator to vector.
 
-        >>> import odl
         >>> X = odl.uniform_discr(0, 1, 3)
         >>> Y = odl.uniform_discr(0, 1, 6)
         >>> resampling = Resampling(X, Y)
@@ -130,7 +128,6 @@ def adjoint(self):
         --------
         Create resampling operator and inverse
 
-        >>> import odl
         >>> X = odl.uniform_discr(0, 1, 3)
         >>> Y = odl.uniform_discr(0, 1, 6)
         >>> resampling = Resampling(X, Y)

odl/discr/discretization.py

@@ -345,9 +345,6 @@ def sampling(self, ufunc, **kwargs):
 
         Examples
         --------
-        >>> import odl
-        >>> import numpy as np
-
         Create discretization
 
         >>> X = odl.uniform_discr(0, 1, 5)
@@ -377,9 +374,6 @@ def interpolation(self):
 
         Examples
         --------
-        >>> import odl
-        >>> import numpy as np
-
         Create continuous version of a discrete 1d function with nearest
         neighbour interpolation:
 

odl/discr/grid.py

@@ -287,8 +287,7 @@ def min(self, **kwargs):
 
         Also works with numpy
 
-        >>> import numpy
-        >>> numpy.min(g)
+        >>> np.min(g)
         array([ 1., -2.])
         """
         out = kwargs.get('out', None)
@@ -319,8 +318,7 @@ def max(self, **kwargs):
 
         Also works with numpy
 
-        >>> import numpy
-        >>> numpy.max(g)
+        >>> np.max(g)
         array([ 5.,  2.])
         """
         out = kwargs.get('out', None)
@@ -1188,8 +1186,7 @@ def uniform_sampling_fromintv(intv_prod, num_nodes, nodes_on_bdry=True):
 
     Examples
     --------
-    >>> from odl import IntervalProd
-    >>> rbox = IntervalProd([-1.5, 2], [-0.5, 3])
+    >>> rbox = odl.IntervalProd([-1.5, 2], [-0.5, 3])
     >>> grid = uniform_sampling_fromintv(rbox, (3, 3))
     >>> grid.coord_vectors
     (array([-1.5, -1. , -0.5]), array([ 2. ,  2.5,  3. ]))

odl/discr/lp_discr.py

@@ -274,7 +274,6 @@ def element(self, inp=None, **kwargs):
         Elements can be created from array-like objects that represent
         an already discretized function:
 
-        >>> import odl
         >>> space = odl.uniform_discr(-1, 1, 4)
         >>> space.element([1, 2, 3, 4])
         uniform_discr(-1.0, 1.0, 4).element([1.0, 2.0, 3.0, 4.0])
@@ -876,8 +875,7 @@ def uniform_discr_frompartition(partition, exponent=2.0, interp='nearest',
 
     Examples
     --------
-    >>> from odl import uniform_partition
-    >>> part = uniform_partition(0, 1, 10)
+    >>> part = odl.uniform_partition(0, 1, 10)
     >>> uniform_discr_frompartition(part)
     uniform_discr(0.0, 1.0, 10)
 
@@ -982,9 +980,8 @@ def uniform_discr_fromspace(fspace, nsamples, exponent=2.0, interp='nearest',
 
     Examples
     --------
-    >>> from odl import Interval, FunctionSpace
-    >>> intv = Interval(0, 1)
-    >>> space = FunctionSpace(intv)
+    >>> intv = odl.Interval(0, 1)
+    >>> space = odl.FunctionSpace(intv)
     >>> uniform_discr_fromspace(space, 10)
     uniform_discr(0.0, 1.0, 10)
 
@@ -1099,8 +1096,7 @@ def uniform_discr_fromintv(interval, nsamples, exponent=2.0, interp='nearest',
 
     Examples
     --------
-    >>> from odl import Interval
-    >>> intv = Interval(0, 1)
+    >>> intv = odl.Interval(0, 1)
     >>> uniform_discr_fromintv(intv, 10)
     uniform_discr(0.0, 1.0, 10)
 

odl/discr/tensor_ops.py

@@ -162,7 +162,6 @@ def __init__(self, vfspace, exponent=None, weight=None):
         standard point-wise norm operator on that space. The operator
         maps a vector field to a scalar function:
 
-        >>> import odl
         >>> spc = odl.uniform_discr([-1, -1], [1, 1], (1, 2))
         >>> vfspace = odl.ProductSpace(spc, 2)
         >>> pw_norm = PointwiseNorm(vfspace)
@@ -509,7 +508,6 @@ def __init__(self, vfspace, vecfield, weight=None):
         point-wise inner product operator with a fixed vector field.
         The operator maps a vector field to a scalar function:
 
-        >>> import odl
         >>> spc = odl.uniform_discr([-1, -1], [1, 1], (1, 2))
         >>> vfspace = odl.ProductSpace(spc, 2)
         >>> fixed_vf = np.array([[[0, 1]],