MAINT: fix invalid escape seqs

package/CHANGELOG

@@ -93,6 +93,8 @@ Changes
   * removes deprecated `as_Universe` function from MDAnalysis.core.universe,
     as a result :class:`MDAnalysis.analysis.leaflet.LeafletFinder` now only
     accepts Universes for its `universe` argument (Issue #2920)
+  * deprecated Python escape sequence usage has been fixed in our test suite,
+    and the test suite will now raise an error for this usage of `\` (PR #2885)
   * deprecated NumPy type aliases have been replaced with their actual types
     (see upstream NumPy PR 14882), and our pytest configuration will raise an
     error for any violation when testing with development NumPy builds

package/MDAnalysis/analysis/data/filenames.py

@@ -20,24 +20,26 @@
 # MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations.
 # J. Comput. Chem. 32 (2011), 2319--2327, doi:10.1002/jcc.21787
 #
-"""Analysis data files
+r"""Analysis data files
 ===================
 
 :mod:`MDAnalysis.analysis.data` contains data files that are used as part of
 analysis. These can be experimental or theoretical data. Files are stored
 inside the package and made accessible via variables in
 :mod:`MDAnalysis.analysis.data.filenames`. These variables are documented
-below, including references to the literature and where they are used
-inside :mod:`MDAnalysis.analysis`.
+below, including references to the literature and where they are used inside
+:mod:`MDAnalysis.analysis`.
 
 Data files
 ----------
 
 .. data:: Rama_ref
 
-   Reference Ramachandran histogram for :class:`MDAnalysis.analysis.dihedrals.Ramachandran`.
-   The data were calculated on a data set of 500 PDB structures taken from [Lovell2003]_.
-   This is a numpy array in the :math:`\phi` and :math:`psi` backbone dihedral angles.
+   Reference Ramachandran histogram for
+   :class:`MDAnalysis.analysis.dihedrals.Ramachandran`.  The data were
+   calculated on a data set of 500 PDB structures taken from [Lovell2003]_.
+   This is a numpy array in the :math:`\phi` and :math:`\psi` backbone dihedral
+   angles.
 
    Load and plot it with ::
 
@@ -55,8 +57,9 @@
 .. data:: Janin_ref
 
    Reference Janin histogram for :class:`MDAnalysis.analysis.dihedrals.Janin`.
-   The data were calculated on a data set of 500 PDB structures taken from [Lovell2003]_.
-   This is a numpy array in the :math:`\chi_1` and :math:`chi_2` sidechain dihedral angles.
+   The data were calculated on a data set of 500 PDB structures taken from
+   [Lovell2003]_.  This is a numpy array in the :math:`\chi_1` and
+   :math:`\chi_2` sidechain dihedral angles.
 
    Load and plot it with ::
 
@@ -73,7 +76,7 @@
 """
 
 __all__ = [
-    "Rama_ref", "Janin_ref" # reference plots for Ramachandran and Janin classes
+    "Rama_ref", "Janin_ref", # reference plots for Ramachandran and Janin classes
 ]
 
 from pkg_resources import resource_filename

package/MDAnalysis/analysis/dihedrals.py

@@ -349,6 +349,7 @@ class Ramachandran(AnalysisBase):
 
     .. versionchanged:: 1.0.0
         added c_name, n_name, ca_name, and check_protein keyword arguments
+
     """
 
     def __init__(self, atomgroup, c_name='C', n_name='N', ca_name='CA',
@@ -422,8 +423,8 @@ def _conclude(self):
     def plot(self, ax=None, ref=False, **kwargs):
         """Plots data into standard Ramachandran plot.
 
-        Each time step in :attr:`Ramachandran.angles` is plotted onto
-        the same graph.
+        Each time step in :attr:`Ramachandran.angles` is plotted onto the same
+        graph.
 
         Parameters
         ----------
@@ -468,8 +469,8 @@ def plot(self, ax=None, ref=False, **kwargs):
 
 
 class Janin(Ramachandran):
-    r"""Calculate :math:`\chi_1` and :math:`\chi_2` dihedral angles of
-    selected residues.
+    r"""Calculate :math:`\chi_1` and :math:`\chi_2` dihedral angles of selected
+    residues.
 
     :math:`\chi_1` and :math:`\chi_2` angles will be calculated for each residue
     corresponding to `atomgroup` for each time step in the trajectory. A

package/MDAnalysis/analysis/encore/clustering/ClusterCollection.py

@@ -101,8 +101,9 @@ def __init__(self, elem_list=None, centroid=None, idn=None, metadata=None):
         if metadata:
             for name, data in metadata.items():
                 if len(data) != self.size:
-                    raise TypeError("Size of metadata having label \"{0}\"\
-is not equal to the number of cluster elmements".format(name))
+                    raise TypeError('Size of metadata having label "{0}"'
+                                    'is not equal to the number of cluster '
+                                    'elements'.format(name))
             self.add_metadata(name, data)
 
     def __iter__(self):
@@ -119,8 +120,8 @@ def __len__(self):
 
     def add_metadata(self, name, data):
         if len(data) != self.size:
-            raise TypeError("Size of metadata is not equal to the number of\
- cluster elmements")
+            raise TypeError("Size of metadata is not equal to the number of"
+                            "cluster elmements")
         self.metadata[name] = np.array(data)
 
     def __repr__(self):
@@ -192,8 +193,9 @@ def __init__(self, elements=None, metadata=None):
         centroids = np.unique(elements_array)
         for i in centroids:
             if elements[i] != i:
-                raise ValueError("element {0}, which is a centroid, doesn't \
-belong to its own cluster".format(elements[i]))
+                raise ValueError("element {0}, which is a centroid, doesn't "
+                                 "belong to its own cluster".format(
+                                     elements[i]))
         for c in centroids:
             this_metadata = {}
             this_array = np.where(elements_array == c)

package/MDAnalysis/analysis/encore/confdistmatrix.py

@@ -171,7 +171,7 @@ def conformational_distance_matrix(ensemble,
     # Initialize workers. Simple worker doesn't perform fitting,
     # fitter worker does.
     indices = trm_indices((0, 0), (framesn - 1, framesn - 1))
-    Parallel(n_jobs=n_jobs, verbose=verbose, require='sharedmem', 
+    Parallel(n_jobs=n_jobs, verbose=verbose, require='sharedmem',
             max_nbytes=max_nbytes)(delayed(conf_dist_function)(
         np.int64(element),
         rmsd_coordinates,
@@ -247,8 +247,8 @@ def set_rmsd_matrix_elements(tasks, coords, rmsdmat, weights, fit_coords=None,
             rotated_i.astype(np.float64), translated_j.astype(np.float64),
             coords[j].shape[0], weights, sumweights)
     else:
-        raise TypeError("Both fit_coords and fit_weights must be specified \
-                        if one of them is given")
+        raise TypeError("Both fit_coords and fit_weights must be specified "
+                        "if one of them is given")
 
 
 def get_distance_matrix(ensemble,

package/MDAnalysis/analysis/encore/covariance.py

@@ -119,7 +119,7 @@ def shrinkage_covariance_estimator( coordinates,
     xmkt = np.average(x, axis=1)
 
     # Call maximum likelihood estimator (note the additional column)
-    sample = ml_covariance_estimator(np.hstack([x, xmkt[:, np.newaxis]]), 0) \
+    sample = ml_covariance_estimator(np.hstack([x, xmkt[:, np.newaxis]]), 0)\
         * (t-1)/float(t)
 
     # Split covariance matrix into components

package/MDAnalysis/analysis/encore/similarity.py

@@ -469,30 +469,33 @@ def gen_kde_pdfs(embedded_space, ensemble_assignment, nensembles,
 def dimred_ensemble_similarity(kde1, resamples1, kde2, resamples2,
                                ln_P1_exp_P1=None, ln_P2_exp_P2=None,
                                ln_P1P2_exp_P1=None, ln_P1P2_exp_P2=None):
-    """
-    Calculate the Jensen-Shannon divergence according the the
-    Dimensionality reduction method. In this case, we have continuous
-    probability densities, this we need to integrate over the measurable
-    space. The aim is to first calculate the Kullback-Liebler divergence, which
-    is defined as:
+    r"""Calculate the Jensen-Shannon divergence according the Dimensionality
+    reduction method.
+
+    In this case, we have continuous probability densities, this we need to
+    integrate over the measurable space. The aim is to first calculate the
+    Kullback-Liebler divergence, which is defined as:
 
     .. math::
-        D_{KL}(P(x) || Q(x)) = \\int_{-\\infty}^{\\infty}P(x_i) ln(P(x_i)/Q(x_i)) = \\langle{}ln(P(x))\\rangle{}_P - \\langle{}ln(Q(x))\\rangle{}_P
 
-    where the :math:`\\langle{}.\\rangle{}_P` denotes an expectation calculated
+       D_{KL}(P(x) || Q(x)) =
+           \int_{-\infty}^{\infty}P(x_i) ln(P(x_i)/Q(x_i)) =
+           \langle{}ln(P(x))\rangle{}_P - \langle{}ln(Q(x))\rangle{}_P
+
+    where the :math:`\langle{}.\rangle{}_P` denotes an expectation calculated
     under the distribution P. We can, thus, just estimate the expectation
-    values of the components to get an estimate of dKL.
-    Since the Jensen-Shannon distance is actually  more complex, we need to
-    estimate four expectation values:
+    values of the components to get an estimate of dKL.  Since the
+    Jensen-Shannon distance is actually more complex, we need to estimate four
+    expectation values:
 
     .. math::
-         \\langle{}log(P(x))\\rangle{}_P
+         \langle{}log(P(x))\rangle{}_P
 
-         \\langle{}log(Q(x))\\rangle{}_Q
+         \langle{}log(Q(x))\rangle{}_Q
 
-         \\langle{}log(0.5*(P(x)+Q(x)))\\rangle{}_P
+         \langle{}log(0.5*(P(x)+Q(x)))\rangle{}_P
 
-         \\langle{}log(0.5*(P(x)+Q(x)))\\rangle{}_Q
+         \langle{}log(0.5*(P(x)+Q(x)))\rangle{}_Q
 
     Parameters
     ----------
@@ -512,22 +515,22 @@ def dimred_ensemble_similarity(kde1, resamples1, kde2, resamples2,
         calculate the expected values according to 'Q' as detailed before.
 
     ln_P1_exp_P1 : float or None
-        Use this value for :math:`\\langle{}log(P(x))\\rangle{}_P`; if None,
+        Use this value for :math:`\langle{}log(P(x))\rangle{}_P`; if ``None``,
         calculate it instead
 
     ln_P2_exp_P2 : float or None
-        Use this value for :math:`\\langle{}log(Q(x))\\rangle{}_Q`; if
-        None, calculate it instead
+        Use this value for :math:`\langle{}log(Q(x))\rangle{}_Q`; if
+        ``None``, calculate it instead
 
     ln_P1P2_exp_P1 : float or None
         Use this value for
-        :math:`\\langle{}log(0.5*(P(x)+Q(x)))\\rangle{}_P`;
-        if None, calculate it instead
+        :math:`\langle{}log(0.5*(P(x)+Q(x)))\rangle{}_P`;
+        if ``None``, calculate it instead
 
     ln_P1P2_exp_P2 : float or None
         Use this value for
-        :math:`\\langle{}log(0.5*(P(x)+Q(x)))\\rangle{}_Q`;
-        if None, calculate it instead
+        :math:`\langle{}log(0.5*(P(x)+Q(x)))\rangle{}_Q`;
+        if ``None``, calculate it instead
 
     Returns
     -------
@@ -720,11 +723,10 @@ def hes(ensembles,
         estimate_error=False,
         bootstrapping_samples=100,
         calc_diagonal=False):
-    """
+    r"""Calculates the Harmonic Ensemble Similarity (HES) between ensembles.
 
-    Calculates the Harmonic Ensemble Similarity (HES) between ensembles using
-    the symmetrized version of Kullback-Leibler divergence as described
-    in [Tiberti2015]_.
+    The HES is calculated with the symmetrized version of Kullback-Leibler
+    divergence as described in [Tiberti2015]_.
 
     Parameters
     ----------
@@ -766,13 +768,13 @@ def hes(ensembles,
     symmetrized version of Kullback-Leibler divergence defined as:
 
     .. math::
-        D_{KL}(P(x) || Q(x)) = \\int_{-\\infty}^{\\infty}P(x_i)
-        ln(P(x_i)/Q(x_i)) = \\langle{}ln(P(x))\\rangle{}_P -
-        \\langle{}ln(Q(x))\\rangle{}_P
+       D_{KL}(P(x) || Q(x)) =
+           \int_{-\infty}^{\infty}P(x_i) ln(P(x_i)/Q(x_i)) =
+           \langle{}ln(P(x))\rangle{}_P - \langle{}ln(Q(x))\rangle{}_P
 
 
-    where the :math:`\\langle{}.\\rangle{}_P` denotes an expectation
-    calculated under the distribution P.
+    where the :math:`\langle{}.\rangle{}_P` denotes an expectation
+    calculated under the distribution :math:`P`.
 
     For each ensemble, the  mean conformation is estimated as the average over
     the ensemble, and the covariance matrix is calculated by default using a
@@ -802,16 +804,17 @@ def hes(ensembles,
          [ 38279683.95892926         0.        ]]
 
 
-    You can use the align=True option to align the ensembles first. This will
+    You can use the ``align=True`` option to align the ensembles first. This will
     align everything to the current timestep in the first ensemble. Note that
-    this changes the ens1 and ens2 objects:
+    this changes the ``ens1`` and ``ens2`` objects:
 
         >>> print(encore.hes([ens1, ens2], align=True)[0])
         [[    0.          6880.34140106]
         [ 6880.34140106     0.        ]]
 
     Alternatively, for greater flexibility in how the alignment should be done
-    you can call use an AlignTraj object manually:
+    you can call use an :class:`~MDAnalysis.analysis.align.AlignTraj` object
+    manually:
 
         >>> from MDAnalysis.analysis import align
         >>> align.AlignTraj(ens1, ens1, select="name CA", in_memory=True).run()
@@ -820,9 +823,10 @@ def hes(ensembles,
         [[    0.          7032.19607004]
          [ 7032.19607004     0.        ]]
 
+
     .. versionchanged:: 1.0.0
-       hes doesn't accept the *details* argument anymore, it always returns the
-       details of the calculation instead, in the form of a dictionary
+       ``hes`` doesn't accept the `details` argument anymore, it always returns
+       the details of the calculation instead, in the form of a dictionary
 
     """
 

package/MDAnalysis/analysis/encore/utils.py

@@ -190,7 +190,7 @@ def __str__(self):
 
 
 class ParallelCalculation(object):
-    """
+    r"""
     Generic parallel calculation class. Can use arbitrary functions,
     arguments to functions and kwargs to functions.
 
@@ -279,7 +279,7 @@ def worker(self, q, results):
             results.put((i, self.functions[i](*self.args[i], **self.kwargs[i])))
 
     def run(self):
-        """
+        r"""
         Run parallel calculation.
 
         Returns

package/MDAnalysis/analysis/gnm.py

@@ -352,7 +352,7 @@ def run(self, start=None, stop=None, step=None):
 
 
 class closeContactGNMAnalysis(GNMAnalysis):
-    """GNMAnalysis only using close contacts.
+    r"""GNMAnalysis only using close contacts.
 
     This is a version of the GNM where the Kirchoff matrix is
     constructed from the close contacts between individual atoms