MAINT: Make several strings case insensitive, see odlgroup#1454 (odlg…

…roup#1463)

* MAINT: Make several strings case insensitive, see odlgroup#1454

* MAINT: pep8 fixes

* MAINT: General PEP8 fixes w.r.t escaped characters

* MAINT: Update copyright year

* MAINT: Standardization of PEP8

.pep8speaks.yml

@@ -16,6 +16,7 @@ pycodestyle:
         - E402
         - E741
         - W503
+        - W504
 
 no_blank_comment: False
 only_mention_files_with_errors: True

examples/solvers/bregman_tv_tomography.py

@@ -42,14 +42,13 @@
 """
 
 import odl
-import numpy as np
 
 # Reconstruction space
 reco_space = odl.uniform_discr(min_pt=[-20, -20], max_pt=[20, 20],
                                shape=[128, 128], dtype='float32')
 
 # Make a parallel beam geometry with flat detector, and create ray transform
-geometry = odl.tomo.parallel_beam_geometry(reco_space,  num_angles=100)
+geometry = odl.tomo.parallel_beam_geometry(reco_space, num_angles=100)
 ray_trafo = odl.tomo.RayTransform(reco_space, geometry)
 
 # Create phantom, forward project to create sinograms, and add 10% noise

examples/solvers/douglas_rachford_pd_heron.py

@@ -1,4 +1,4 @@
-"""Solves the generalized Heron problem using the Douglas-Rachford solver.
+r"""Solves the generalized Heron problem using the Douglas-Rachford solver.
 
 The generalized Heron problem is defined as
 

odl/contrib/fom/supervised.py

@@ -223,7 +223,7 @@ def mean_value_difference(data, ground_truth, mask=None, normalized=False,
 def standard_deviation_difference(data, ground_truth, mask=None,
                                   normalized=False,
                                   force_lower_is_better=True):
-    r"""Return absolute difference in std between ``data`` and ``ground_truth``.
+    r"""Return absolute diff in std between ``data`` and ``ground_truth``.
 
     Parameters
     ----------

odl/contrib/fom/util.py

@@ -1,4 +1,4 @@
-# Copyright 2014-2018 The ODL contributors
+# Copyright 2014-2019 The ODL contributors
 #
 # This file is part of ODL.
 #
@@ -51,6 +51,10 @@ def filter_image_sep2d(image, fh, fv, impl='numpy', padding=None):
     """
     # TODO: generalize for nD
     impl, impl_in = str(impl).lower(), impl
+    if impl not in ('numpy', 'pyfftw'):
+        raise ValueError('`impl` {!r} not understood'
+                         ''.format(impl_in))
+
     image = np.asarray(image)
     if image.ndim != 2:
         raise ValueError('`image` must be 2-dimensional, got image with '
@@ -185,7 +189,7 @@ def prepare_for_fft(filt, n_new):
 
 
 def haarpsi_similarity_map(img1, img2, axis, c, a):
-    """Local similarity map for directional features along an axis.
+    r"""Local similarity map for directional features along an axis.
 
     Parameters
     ----------
@@ -214,11 +218,11 @@ def haarpsi_similarity_map(img1, img2, axis, c, a):
 
     .. math::
         \mathrm{HS}_{f_1, f_2}^{(k)}(x) =
-        l_a \\left(
-        \\frac{1}{2} \sum_{j=1}^2
-        S\\left(\\left|g_j^{(k)} \\ast f_1 \\right|(x),
-        \\left|g_j^{(k)} \\ast f_2 \\right|(x), c\\right)
-        \\right),
+        l_a \left(
+        \frac{1}{2} \sum_{j=1}^2
+        S\left(\left|g_j^{(k)} \ast f_1 \right|(x),
+        \left|g_j^{(k)} \ast f_2 \right|(x), c\right)
+        \right),
 
     see `[Rei+2016] <https://arxiv.org/abs/1607.06140>`_ equation (10).
     Here, the superscript :math:`(k)` refers to the axis (0 or 1)
@@ -227,7 +231,7 @@ def haarpsi_similarity_map(img1, img2, axis, c, a):
     is the pointwise similarity score
 
     .. math::
-        S(x, y, c) = \\frac{2xy + c^2}{x^2 + y^2 + c^2},
+        S(x, y, c) = \frac{2xy + c^2}{x^2 + y^2 + c^2},
 
     Hence, :math:`c` is the :math:`y`-value at which the score
     drops to :math:`1 / 2` for :math:`x = 0`. In other words, the smaller
@@ -247,7 +251,7 @@ def haarpsi_similarity_map(img1, img2, axis, c, a):
         ...
 
     The logistic function :math:`l_a` transforms values in
-    :math:`[0, \\infty)` to :math:`[1/2, 1)`, where the parameter
+    :math:`[0, \infty)` to :math:`[1/2, 1)`, where the parameter
     :math:`a` determines how fast the curve attains values close
     to 1. Larger :math:`a` means that smaller :math:`x` will yield
     a value :math:`l_a(x)` close to 1 (and thus result in a higher
@@ -324,7 +328,7 @@ def S(x, y):
 
 
 def haarpsi_weight_map(img1, img2, axis):
-    """Weighting map for directional features along an axis.
+    r"""Weighting map for directional features along an axis.
 
     Parameters
     ----------
@@ -346,10 +350,10 @@ def haarpsi_weight_map(img1, img2, axis):
 
     .. math::
         \mathrm{W}_{f_1, f_2}^{(k)}(x) =
-        \max \\left\{
-        \\left|g_3^{(k)} \\ast f_1 \\right|(x),
-        \\left|g_3^{(k)} \\ast f_2 \\right|(x)
-        \\right\},
+        \max \left\{
+            \left|g_3^{(k)} \ast f_1 \right|(x),
+            \left|g_3^{(k)} \ast f_2 \right|(x)
+        \right\},
 
     see `[Rei+2016] <https://arxiv.org/abs/1607.06140>`_ equations (11)
     and (13).

odl/contrib/mrc/mrc.py

@@ -1,4 +1,4 @@
-# Copyright 2014-2017 The ODL contributors
+# Copyright 2014-2019 The ODL contributors
 #
 # This file is part of ODL.
 #
@@ -122,6 +122,7 @@ def print_mrc2014_spec():
     """
     print(MRC_2014_SPEC_TABLE)
 
+
 print_mrc2014_spec.__doc__ += MRC_2014_SPEC_TABLE
 
 
@@ -193,6 +194,7 @@ def print_fei_ext_header_spec():
     """
     print(MRC_FEI_EXT_HEADER_SECTION)
 
+
 print_fei_ext_header_spec.__doc__ += MRC_FEI_EXT_HEADER_SECTION
 
 

odl/contrib/mrc/test/uncompr_bin_test.py

@@ -38,6 +38,7 @@
 def test_uncompr_bin_io_without_header(shape, floating_dtype, order):
     """Test I/O bypassing the header processing."""
     dtype = np.dtype(floating_dtype)
+    order = str(order).upper()
     with tempfile.NamedTemporaryFile() as named_file:
         file = named_file.file
 

odl/contrib/solvers/functional/nonlocalmeans_functionals.py

@@ -1,4 +1,4 @@
-# Copyright 2014-2017 The ODL contributors
+# Copyright 2014-2019 The ODL contributors
 #
 # This file is part of ODL.
 #
@@ -19,7 +19,7 @@
 
 class NLMRegularizer(Functional):
 
-    """The nonlocal means "functional".
+    r"""The nonlocal means "functional".
 
     This is not a true functional in the strict sense, but regardless it
     implements a `proximal` method and is hence usable with proximal solvers.
@@ -40,9 +40,9 @@ class NLMRegularizer(Functional):
 
     .. math::
         NL[u](x) =
-            \\frac{1}{C(x)}
-            \\int_\\Omega
-            e^{-\\frac{(G_a * |u(x + \cdot) - u(y + \cdot)|^2)(0)}{h^2}}
+            \frac{1}{C(x)}
+            \int_\Omega
+            e^{-\frac{(G_a * |u(x + \cdot) - u(y + \cdot)|^2)(0)}{h^2}}
             u(y) dy
 
     where :math:`\Omega` is the domain, :math:`G_a` is a gaussian kernel,
@@ -51,8 +51,8 @@ class NLMRegularizer(Functional):
 
     .. math::
         C(x) =
-        \\int_\\Omega
-        e^{-\\frac{(G_a * |u(x + \cdot) - u(y + \cdot)|^2)(0)}{h^2}}
+        \int_\Omega
+        e^{-\frac{(G_a * |u(x + \cdot) - u(y + \cdot)|^2)(0)}{h^2}}
         dy
 
     See [Buades+2005] for more information.

odl/contrib/solvers/spdhg/examples/PET_1k.py

@@ -1,4 +1,4 @@
-# Copyright 2014-2018 The ODL contributors
+# Copyright 2014-2019 The ODL contributors
 #
 # This file is part of ODL.
 #
@@ -331,7 +331,7 @@ def fun_select(k):
 iter_save_v, niter_v, image_v, out_v, nsub_v = {}, {}, {}, {}, {}
 for a in algs:
     (iter_save_v[a], niter_v[a], image_v[a], out_v[a], nsub_v[a]) = np.load(
-     '{}/{}_output.npy'.format(folder_npy, a))
+        '{}/{}_output.npy'.format(folder_npy, a))
 
 epochs_save = {a: np.array(iter_save_v[a]) / np.float(nsub_v[a]) for a in algs}
 
@@ -444,8 +444,8 @@ def fun_select(k):
 msize = 9  # marker size
 
 algs = ['pdhg', 'spdhg10', 'spdhg50', 'pesquet10', 'pesquet50']
-label = ['PDHG', 'SPDHG (10 subsets)', 'SPDHG (50)', 'Pesquet\&Repetti (10)',
-         'Pesquet\&Repetti (50)']
+label = ['PDHG', 'SPDHG (10 subsets)', 'SPDHG (50)', 'Pesquet\\&Repetti (10)',
+         'Pesquet\\&Repetti (50)']
 fig = []
 
 # ### draw figures

odl/contrib/solvers/spdhg/examples/PET_linear_rate.py

@@ -1,4 +1,4 @@
-# Copyright 2014-2018 The ODL contributors
+# Copyright 2014-2019 The ODL contributors
 #
 # This file is part of ODL.
 #
@@ -438,8 +438,8 @@ def fun_select(k):
 msize = 9  # marker size
 
 algs = ['pdhg', 'spdhg_uni10', 'spdhg_uni50', 'pesquet_uni10', 'pesquet_uni50']
-label = ['PDHG', 'SPDHG (10 subsets)', 'SPDHG (50)', 'Pesquet\&Repetti (10)',
-         'Pesquet\&Repetti (50)']
+label = ['PDHG', 'SPDHG (10 subsets)', 'SPDHG (50)', 'Pesquet\\&Repetti (10)',
+         'Pesquet\\&Repetti (50)']
 fig = []
 
 # draw figures

odl/contrib/solvers/spdhg/examples/ROF_1k2_primal.py

@@ -1,4 +1,4 @@
-# Copyright 2014-2018 The ODL contributors
+# Copyright 2014-2019 The ODL contributors
 #
 # This file is part of ODL.
 #
@@ -384,7 +384,7 @@ def fun_select(k):
 msize = 9  # marker size
 
 algs = ['pdhg', 'pa_pdhg', 'spdhg_uni2', 'pa_spdhg_uni2', 'pesquet_uni2']
-label = ['PDHG', 'PA-PDHG', 'SPDHG', 'PA-SPDHG', 'Pesquet\&Repetti']
+label = ['PDHG', 'PA-PDHG', 'SPDHG', 'PA-SPDHG', 'Pesquet\\&Repetti']
 fig = []
 
 # draw first figure

odl/contrib/solvers/spdhg/misc.py

@@ -1,4 +1,4 @@
-# Copyright 2014-2018 The ODL contributors
+# Copyright 2014-2019 The ODL contributors
 #
 # This file is part of ODL.
 #
@@ -441,20 +441,21 @@ def __repr__(self):
 
 
 class KullbackLeiblerSmooth(odl.solvers.Functional):
-    """The smooth Kullback-Leibler divergence functional.
+
+    r"""The smooth Kullback-Leibler divergence functional.
 
     Notes
     -----
     If the functional is defined on an :math:`\mathbb{R}^n`-like space, the
-    smooth Kullback-Leibler functional :math:`\\phi` is defined as
+    smooth Kullback-Leibler functional :math:`\phi` is defined as
 
     .. math::
-        \\phi(x) = \\sum_{i=1}^n \\begin{cases}
-                x + r - y + y * \\log(y / (x + r))
-                    & \\text{if $x \geq 0$} \\
+        \phi(x) = \sum_{i=1}^n \begin{cases}
+                x + r - y + y * \log(y / (x + r))
+                    & \text{if $x \geq 0$} \
                 (y / (2 * r^2)) * x^2 + (1 - y / r) * x + r - b +
-                    b * \\log(b / r) & \\text{else}
-                                 \\end{cases}
+                    b * \log(b / r) & \text{else}
+                                 \end{cases}
 
     where all variables on the right hand side of the equation have a subscript
     i which is omitted for readability.
@@ -561,24 +562,25 @@ def __repr__(self):
 
 
 class KullbackLeiblerSmoothConvexConj(odl.solvers.Functional):
-    """The convex conjugate of the smooth Kullback-Leibler divergence functional.
+
+    r"""The convex conj of the smooth Kullback-Leibler divergence functional.
 
     Notes
     -----
     If the functional is defined on an :math:`\mathbb{R}^n`-like space, the
-    convex conjugate of the smooth Kullback-Leibler functional :math:`\\phi^*`
+    convex conjugate of the smooth Kullback-Leibler functional :math:`\phi^*`
     is defined as
 
     .. math::
-        \\phi^*(x) = \\sum_{i=1}^n \\begin{cases}
+        \phi^*(x) = \sum_{i=1}^n \begin{cases}
                 r^2 / (2 * y) * x^2 + (r - r^2 / y) * x + r^2 / (2 * y) +
                     3 / 2 * y - 2 * r - y * log(y / r)
-                    & \\text{if $x < 1 - y / r$} \\
+                    & \text{if $x < 1 - y / r$} \
                 - r * x - y * log(1 - x)
-                    & \\text{if $1 - y / r <= x < 1} \\
+                    & \text{if $1 - y / r <= x < 1} \
                 + \infty
-                    & \\text{else}
-                                 \\end{cases}
+                    & \text{else}
+                                 \end{cases}
 
     where all variables on the right hand side of the equation have a subscript
     :math:`i` which is omitted for readability.