minor clean ups

- travis.yml - docs, code style in analysis and analysis tests
MDAnalysis · Jun 18, 2017 · f8d5c5e · f8d5c5e
1 parent 1a309eb
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diff --git a/.travis.yml b/.travis.yml
@@ -27,7 +27,6 @@ env:
     - BUILD_CMD="pip install -v package/ && pip install testsuite/"
     - CONDA_DEPENDENCIES="mmtf-python nose=1.3.7 mock six biopython networkx cython joblib nose-timer matplotlib scipy griddataformats"
     - CONDA_ALL_DEPENDENCIES="mmtf-python nose=1.3.7 mock six biopython networkx cython joblib nose-timer matplotlib netcdf4 scikit-learn scipy griddataformats seaborn coveralls clustalw=2.1"
-    - PIP_DEPENDENCIES=""
     - CONDA_CHANNELS='biobuilds conda-forge'
     - CONDA_CHANNEL_PRIORITY=True
     - NUMPY_VERSION=stable

diff --git a/package/MDAnalysis/analysis/hbonds/hbond_autocorrel.py b/package/MDAnalysis/analysis/hbonds/hbond_autocorrel.py
@@ -423,6 +423,7 @@ 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.

diff --git a/package/MDAnalysis/analysis/psa.py b/package/MDAnalysis/analysis/psa.py
@@ -409,26 +409,27 @@ def hausdorff(P, Q):
     still has to calculate the *symmetric* Hausdorff distance as
     `max(directed_hausdorff(P, Q)[0], directed_hausdorff(Q, P)[0])`.
 
+
     References
     ----------
     .. [Huttenlocher1993] D. P. Huttenlocher, G. A. Klanderman, and
         W. J. Rucklidge. Comparing images using the Hausdorff distance. IEEE
         Transactions on Pattern Analysis and Machine Intelligence,
         15(9):850–863, 1993.
-
     .. [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
-    -------
+
+    See Also
+    --------
     scipy.spatial.distance.directed_hausdorff
 
     """
     N, axis = get_coord_axes(P)
     d = get_msd_matrix(P, Q, axis=axis)
-    return ( max( np.amax(np.amin(d, axis=0)),                                  \
-                  np.amax(np.amin(d, axis=1)) ) / N  )**0.5
+    return (max(np.amax(np.amin(d, axis=0)),
+                np.amax(np.amin(d, axis=1))) / N)**0.5
 
 
 def hausdorff_wavg(P, Q):

diff --git a/testsuite/MDAnalysisTests/analysis/test_encore.py b/testsuite/MDAnalysisTests/analysis/test_encore.py
@@ -118,18 +118,18 @@ def test_triangular_matrix():
 
         incremented_triangular_matrix = triangular_matrix + scalar
         assert_equal(incremented_triangular_matrix[0,1], expected_value + scalar,
-                        err_msg="Error in TriangularMatrix: addition of scalar gave\
-inconsistent results")
+                     err_msg="Error in TriangularMatrix: addition of scalar gave"
+                     "inconsistent results")
 
         triangular_matrix += scalar
         assert_equal(triangular_matrix[0,1], expected_value + scalar,
-                        err_msg="Error in TriangularMatrix: addition of scalar gave\
-inconsistent results")
+                     err_msg="Error in TriangularMatrix: addition of scalar gave"
+                     "inconsistent results")
 
         multiplied_triangular_matrix_2 = triangular_matrix_2 * scalar
         assert_equal(multiplied_triangular_matrix_2[0,1], expected_value * scalar,
-            err_msg="Error in TriangularMatrix: multiplication by scalar gave\
-inconsistent results")
+                     err_msg="Error in TriangularMatrix: multiplication by scalar gave"
+                     "inconsistent results")
 
         triangular_matrix_2 *= scalar
         assert_equal(triangular_matrix_2[0,1], expected_value * scalar,