scipy and matplotlib are imported at top in analysis

- updated all modules
- removed any code that guards against scipy or matplotlib import
- conforms to style guide https://github.com/MDAnalysis/mdanalysis/wiki/Style-Guide#module-imports-in-mdanalysisanalysis
- fixes MDAnalysis#1159
- fixes MDAnalysis#1361

package/MDAnalysis/analysis/hbonds/hbond_autocorrel.py

@@ -436,7 +436,6 @@ def solve(self, p_guess=None):
             Initial guess for the leastsq fit, must match the shape of the
             expected coefficients
 
-
         Continuous defition results are fitted to a double exponential with
         :func:`scipy.optimize.leastsq`, intermittent definition are fit to a
         triple exponential.

package/MDAnalysis/analysis/hole.py

@@ -274,6 +274,10 @@
 import matplotlib
 import matplotlib.pyplot as plt
 
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+
 from MDAnalysis import Universe
 from MDAnalysis.exceptions import ApplicationError
 from MDAnalysis.lib.util import (which, realpath, asiterable,

package/MDAnalysis/analysis/psa.py

@@ -223,9 +223,11 @@
 
 import numpy as np
 from scipy import spatial, cluster
-from scipy.spatial.distance import directed_hausdorff
 import matplotlib
 
+import warnings
+import numbers
+
 import MDAnalysis
 import MDAnalysis.analysis.align
 from MDAnalysis import NoDataError
@@ -427,17 +429,25 @@ def hausdorff(P, Q):
 
     Notes
     -----
+    The Hausdorff distance is calculated in a brute force manner from the
+    distance matrix without further optimizations, essentially following
+    [Huttenlocher1993]_.
+
     :func:`scipy.spatial.distance.directed_hausdorff` is an optimized
-    implementation of the early break algorithm of [Taha2015]_; the
-    latter code is used here to calculate the symmetric Hausdorff
-    distance with an RMSD metric
+    implementation of the early break algorithm of [Taha2015]_; note that one
+    still has to calculate the *symmetric* Hausdorff distance as
+    `max(directed_hausdorff(P, Q)[0], directed_hausdorff(Q, P)[0])`.
 
     References
     ----------
     .. [Taha2015] A. A. Taha and A. Hanbury. An efficient algorithm for
        calculating the exact Hausdorff distance. IEEE Transactions On Pattern
        Analysis And Machine Intelligence, 37:2153-63, 2015.
 
+    SeeAlso
+    -------
+    scipy.spatial.distance.directed_hausdorff
+
     """
     N_p, axis_p = get_coord_axes(P)
     N_q, axis_q = get_coord_axes(Q)
@@ -2017,6 +2027,8 @@ def plot_nearest_neighbors(self, filename=None, idx=0,                      \
 
         return ax
 
+        return ax
+
 
     def cluster(self, dist_mat=None, method='ward', count_sort=False,           \
                 distance_sort=False, no_plot=False, no_labels=True,             \
@@ -2067,13 +2079,7 @@ def cluster(self, dist_mat=None, method='ward', count_sort=False,           \
         orig_linewidth = matplotlib.rcParams['lines.linewidth']
         matplotlib.rcParams['lines.linewidth'] = 0.5
         try:
-            if dist_mat:
-                dist_vec = spatial.distance.squareform(dist_mat,
-                                                       force='tovector',
-                                                       checks=True)
-            else:
-                dist_vec = self.get_pairwise_distances(vectorform=True)
-            Z = cluster.hierarchy.linkage(dist_vec, method=method)
+            Z = cluster.hierarchy.linkage(distArray, method=method)
             dgram = cluster.hierarchy.dendrogram(
                 Z, no_labels=no_labels, orientation='left',
                 count_sort=count_sort, distance_sort=distance_sort,
@@ -2117,7 +2123,7 @@ def get_num_atoms(self):
         Returns
         -------
         int
-            the number of atoms in any path
+            the number of atoms
 
         Note
         ----
@@ -2194,8 +2200,7 @@ def get_pairwise_distances(self, vectorform=False, checks=False):
             raise ValueError(
                 "No distance data; do 'PSAnalysis.run(store=True)' first.")
         if vectorform:
-            return spatial.distance.squareform(self.D, force='tovector',
-                                               checks=checks)
+            return spatial.distance.squareform(self.D)
         else:
             return self.D