Moves msd results to use the Results class (#3265)

Towards #3261

## Work done in this PR
* Moves msd results to use the Results class

Co-authored-by: Oliver Beckstein <[email protected]>

package/CHANGELOG

@@ -174,6 +174,8 @@ Enhancements
     checking if it can be used in parallel analysis. (Issue #2996, PR #2950)
 
 Changes
+  * `msd.EinsteinMSD` now uses the `analysis.base.Results` class to store
+    analysis results (Issue #3261)
   * `bat.BAT` now uses the `analysis.base.Results` class to store the `bat`
     results attribute (Issue #3261, #3272)
   * `contacts.Contacts` now stores data using the `Contacts.results.timeseries`

package/MDAnalysis/analysis/msd.py

@@ -90,7 +90,7 @@
 
 .. code-block:: python
 
-    msd =  MSD.timeseries
+    msd =  MSD.results.timeseries
 
 Visual inspection of the MSD is important, so let's take a look at it with a
  simple plot.
@@ -261,16 +261,19 @@ class EinsteinMSD(AnalysisBase):
     ----------
     dim_fac : int
         Dimensionality :math:`d` of the MSD.
-    timeseries : :class:`numpy.ndarray`
+    results.timeseries : :class:`numpy.ndarray`
         The averaged MSD over all the particles with respect to lag-time.
-    msds_by_particle : :class:`numpy.ndarray`
+    results.msds_by_particle : :class:`numpy.ndarray`
         The MSD of each individual particle with respect to lag-time.
     ag : :class:`AtomGroup`
         The :class:`AtomGroup` resulting from your selection
     n_frames : int
         Number of frames included in the analysis.
     n_particles : int
         Number of particles MSD was calculated over.
+
+
+    .. versionadded:: 2.0.0
     """
 
     def __init__(self, u, select='all', msd_type='xyz', fft=True, **kwargs):
@@ -307,16 +310,17 @@ def __init__(self, u, select='all', msd_type='xyz', fft=True, **kwargs):
         self._position_array = None
 
         # result
-        self.msds_by_particle = None
-        self.timeseries = None
+        self.results.msds_by_particle = None
+        self.results.timeseries = None
 
     def _prepare(self):
         # self.n_frames only available here
         # these need to be zeroed prior to each run() call
-        self.msds_by_particle = np.zeros((self.n_frames, self.n_particles))
+        self.results.msds_by_particle = np.zeros((self.n_frames,
+                                                  self.n_particles))
         self._position_array = np.zeros(
             (self.n_frames, self.n_particles, self.dim_fac))
-        # self.timeseries not set here
+        # self.results.timeseries not set here
 
     def _parse_msd_type(self):
         r""" Sets up the desired dimensionality of the MSD.
@@ -360,8 +364,8 @@ def _conclude_simple(self):
         for lag in lagtimes:
             disp = positions[:-lag, :, :] - positions[lag:, :, :]
             sqdist = np.square(disp).sum(axis=-1)
-            self.msds_by_particle[lag, :] = np.mean(sqdist, axis=0)
-        self.timeseries = self.msds_by_particle.mean(axis=1)
+            self.results.msds_by_particle[lag, :] = np.mean(sqdist, axis=0)
+        self.results.timeseries = self.results.msds_by_particle.mean(axis=1)
 
     def _conclude_fft(self):  # with FFT, np.float64 bit prescision required.
         r""" Calculates the MSD via the FCA fast correlation algorithm.
@@ -382,6 +386,6 @@ def _conclude_fft(self):  # with FFT, np.float64 bit prescision required.
 
         positions = self._position_array.astype(np.float64)
         for n in range(self.n_particles):
-            self.msds_by_particle[:, n] = tidynamics.msd(
+            self.results.msds_by_particle[:, n] = tidynamics.msd(
                 positions[:, n, :])
-        self.timeseries = self.msds_by_particle.mean(axis=1)
+        self.results.timeseries = self.results.msds_by_particle.mean(axis=1)

testsuite/MDAnalysisTests/analysis/test_msd.py

@@ -123,7 +123,7 @@ def test_simple_step_traj_all_dims(self, step_traj, NSTEP, dim,
         m_simple = MSD(step_traj, 'all', msd_type=dim, fft=False)
         m_simple.run()
         poly = characteristic_poly(NSTEP, dim_factor)
-        assert_almost_equal(m_simple.timeseries, poly, decimal=4)
+        assert_almost_equal(m_simple.results.timeseries, poly, decimal=4)
 
     @pytest.mark.parametrize("dim, dim_factor", [
         ('xyz', 3), ('xy', 2), ('xz', 2), ('yz', 2), ('x', 1), ('y', 1),
@@ -137,13 +137,14 @@ def test_simple_start_stop_step_all_dims(self, step_traj, NSTEP, dim,
         m_simple.run(start=10, stop=1000, step=10)
         poly = characteristic_poly(NSTEP, dim_factor)
         # polynomial must take offset start into account
-        assert_almost_equal(m_simple.timeseries, poly[0:990:10], decimal=4)
+        assert_almost_equal(m_simple.results.timeseries, poly[0:990:10],
+                            decimal=4)
 
     def test_random_walk_u_simple(self, random_walk_u):
         # regress against random_walk test data
         msd_rw = MSD(random_walk_u, 'all', msd_type='xyz', fft=False)
         msd_rw.run()
-        norm = np.linalg.norm(msd_rw.timeseries)
+        norm = np.linalg.norm(msd_rw.results.timeseries)
         val = 3932.39927487146
         assert_almost_equal(norm, val, decimal=5)
 
@@ -161,25 +162,25 @@ def msd_fft(self, u, SELECTION):
 
     def test_fft_vs_simple_default(self, msd, msd_fft):
         # testing on the  PSF, DCD trajectory
-        timeseries_simple = msd.timeseries
-        timeseries_fft = msd_fft.timeseries
+        timeseries_simple = msd.results.timeseries
+        timeseries_fft = msd_fft.results.timeseries
         assert_almost_equal(timeseries_simple, timeseries_fft, decimal=4)
 
     def test_fft_vs_simple_default_per_particle(self, msd, msd_fft):
         # check fft and simple give same result per particle
-        per_particle_simple = msd.msds_by_particle
-        per_particle_fft = msd_fft.msds_by_particle
+        per_particle_simple = msd.results.msds_by_particle
+        per_particle_fft = msd_fft.results.msds_by_particle
         assert_almost_equal(per_particle_simple, per_particle_fft, decimal=4)
 
     @pytest.mark.parametrize("dim", ['xyz', 'xy', 'xz', 'yz', 'x', 'y', 'z'])
     def test_fft_vs_simple_all_dims(self, u, SELECTION, dim):
         # check fft and simple give same result for each dimensionality
         m_simple = MSD(u, SELECTION, msd_type=dim, fft=False)
         m_simple.run()
-        timeseries_simple = m_simple.timeseries
+        timeseries_simple = m_simple.results.timeseries
         m_fft = MSD(u, SELECTION, msd_type=dim, fft=True)
         m_fft.run()
-        timeseries_fft = m_fft.timeseries
+        timeseries_fft = m_fft.results.timeseries
         assert_almost_equal(timeseries_simple, timeseries_fft, decimal=4)
 
     @pytest.mark.parametrize("dim", ['xyz', 'xy', 'xz', 'yz', 'x', 'y', 'z'])
@@ -188,10 +189,10 @@ def test_fft_vs_simple_all_dims_per_particle(self, u, SELECTION, dim):
         # dimension
         m_simple = MSD(u, SELECTION, msd_type=dim, fft=False)
         m_simple.run()
-        per_particle_simple = m_simple.msds_by_particle
+        per_particle_simple = m_simple.results.msds_by_particle
         m_fft = MSD(u, SELECTION, msd_type=dim, fft=True)
         m_fft.run()
-        per_particle_fft = m_fft.msds_by_particle
+        per_particle_fft = m_fft.results.msds_by_particle
         assert_almost_equal(per_particle_simple, per_particle_fft, decimal=4)
 
     @pytest.mark.parametrize("dim, dim_factor", [
@@ -208,7 +209,7 @@ def test_fft_step_traj_all_dims(self, step_traj, NSTEP, dim, dim_factor):
         m_simple.run()
         poly = characteristic_poly(NSTEP, dim_factor)
         # this was relaxed from decimal=4 for numpy=1.13 test
-        assert_almost_equal(m_simple.timeseries, poly, decimal=3)
+        assert_almost_equal(m_simple.results.timeseries, poly, decimal=3)
 
     @pytest.mark.parametrize("dim, dim_factor", [(
         'xyz', 3), ('xy', 2), ('xz', 2), ('yz', 2), ('x', 1), ('y', 1),
@@ -222,12 +223,13 @@ def test_fft_start_stop_step_all_dims(self, step_traj, NSTEP, dim,
         m_simple.run(start=10, stop=1000, step=10)
         poly = characteristic_poly(NSTEP, dim_factor)
         # polynomial must take offset start into account
-        assert_almost_equal(m_simple.timeseries, poly[0:990:10], decimal=3)
+        assert_almost_equal(m_simple.results.timeseries, poly[0:990:10],
+                            decimal=3)
 
     def test_random_walk_u_fft(self, random_walk_u):
         # regress against random_walk test data
         msd_rw = MSD(random_walk_u, 'all', msd_type='xyz', fft=True)
         msd_rw.run()
-        norm = np.linalg.norm(msd_rw.timeseries)
+        norm = np.linalg.norm(msd_rw.results.timeseries)
         val = 3932.39927487146
         assert_almost_equal(norm, val, decimal=5)