From 32323933b10ccfdf38d614d03f2438f432d3ba6e Mon Sep 17 00:00:00 2001
From: Philip Loche <ploche@physik.fu-berlin.de>
Date: Fri, 7 May 2021 12:14:32 +0200
Subject: [PATCH 01/17] Use results for pca

---
 package/MDAnalysis/analysis/pca.py            | 160 +++++++++++++-----
 .../MDAnalysisTests/analysis/test_pca.py      |  20 +--
 2 files changed, 124 insertions(+), 56 deletions(-)

diff --git a/package/MDAnalysis/analysis/pca.py b/package/MDAnalysis/analysis/pca.py
index c4e8b7b9afe..88e69cdfd72 100644
--- a/package/MDAnalysis/analysis/pca.py
+++ b/package/MDAnalysis/analysis/pca.py
@@ -69,17 +69,21 @@
 
 First load all modules and test data
 
-    >>> import MDAnalysis as mda
-    >>> import MDAnalysis.analysis.pca as pca
-    >>> from MDAnalysis.tests.datafiles import PSF, DCD
+.. code-block:: python
+    import MDAnalysis as mda
+    import MDAnalysis.analysis.pca as pca
+    rom MDAnalysis.tests.datafiles import PSF, DCD
+
 
 Given a universe containing trajectory data we can perform Principal Component
 Analyis by using the class :class:`PCA` and retrieving the principal
 components.
 
-    >>> u = mda.Universe(PSF, DCD)
-    >>> PSF_pca = pca.PCA(u, select='backbone')
-    >>> PSF_pca.run()
+.. code-block:: python
+    u = mda.Universe(PSF, DCD)
+    PSF_pca = pca.PCA(u, select='backbone')
+    PSF_pca.run()
+
 
 Inspect the components to determine the principal components you would like
 to retain. The choice is arbitrary, but I will stop when 95 percent of the
@@ -88,9 +92,11 @@
 ``cumulated_variance``. The value at the ith index of `cumulated_variance`
 is the sum of the variances from 0 to i.
 
-    >>> n_pcs = np.where(PSF_pca.cumulated_variance > 0.95)[0][0]
-    >>> atomgroup = u.select_atoms('backbone')
-    >>> pca_space = PSF_pca.transform(atomgroup, n_components=n_pcs)
+.. code-block:: python
+    n_pcs = np.where(PSF_pca.cumulated_variance > 0.95)[0][0]
+    atomgroup = u.select_atoms('backbone')
+    pca_space = PSF_pca.transform(atomgroup, n_components=n_pcs)
+
 
 From here, inspection of the ``pca_space`` and conclusions to be drawn from the
 data are left to the user.
@@ -123,32 +129,83 @@ class PCA(AnalysisBase):
     principal components ordering the atom coordinate data by decreasing
     variance will be available for analysis. As an example:
 
-        >>> pca = PCA(universe, select='backbone').run()
-        >>> pca_space =  pca.transform(universe.select_atoms('backbone'), 3)
+    .. code-block:: python
+        pca = PCA(universe, select='backbone').run()
+        pca_space = pca.transform(universe.select_atoms('backbone'), 3)
+
 
     generates the principal components of the backbone of the atomgroup and
     then transforms those atomgroup coordinates by the direction of those
     variances. Please refer to the :ref:`PCA-tutorial` for more detailed
     instructions.
 
+    Parameters
+    ----------
+    universe : Universe
+        Universe
+    select : string, optional
+        A valid selection statement for choosing a subset of atoms from
+        the atomgroup.
+    align : boolean, optional
+        If True, the trajectory will be aligned to a reference
+        structure.
+    mean : MDAnalysis atomgroup, optional
+        An optional reference structure to be used as the mean of the
+        covariance matrix.
+    n_components : int, optional
+        The number of principal components to be saved, default saves
+        all principal components
+    verbose : bool (optional)
+            Show detailed progress of the calculation if set to ``True``.
+
     Attributes
     ----------
-    p_components: array, (n_atoms * 3, n_components)
-        The principal components of the feature space,
+    results.p_components: array, (n_atoms * 3, n_components)
+        Principal components of the feature space,
         representing the directions of maximum variance in the data.
         The column vector p_components[:, i] is the eigenvector
         corresponding to the variance[i].
-    variance : array (n_components, )
-        The raw variance explained by each eigenvector of the covariance
+
+    p_components: array, (n_atoms * 3, n_components)
+        Alias to the :attr:`results.p_components`.
+
+        .. deprecated:: 2.0.0
+                Will be removed in MDAnalysis 3.0.0. Please use
+                :attr:`results.density` instead.
+
+    results.variance : array (n_components, )
+        Raw variance explained by each eigenvector of the covariance
         matrix.
-    cumulated_variance : array, (n_components, )
+
+    variance : array (n_components, )
+        Alias to the :attr:`results.variance`.
+
+        .. deprecated:: 2.0.0
+                Will be removed in MDAnalysis 3.0.0. Please use
+                :attr:`results.density` instead.
+
+    results.cumulated_variance : array, (n_components, )
         Percentage of variance explained by the selected components and the sum
         of the components preceding it. If a subset of components is not chosen
         then all components are stored and the cumulated variance will converge
         to 1.
+
+    cumulated_variance : array, (n_components, )
+        Alias to the :attr:`results.cumulated_variance`.
+
+        .. deprecated:: 2.0.0
+                Will be removed in MDAnalysis 3.0.0. Please use
+                :attr:`results.density` instead.
+
+    results.mean_atoms: MDAnalyis atomgroup
+        Atoms used for the creation of the covariance matrix.
+
     mean_atoms: MDAnalyis atomgroup
-        After running :meth:`PCA.run`, the mean position of all the atoms
-        used for the creation of the covariance matrix will exist here.
+        Alias to the :attr:`results.mean_atoms`.
+
+        .. deprecated:: 2.0.0
+                Will be removed in MDAnalysis 3.0.0. Please use
+                :attr:`results.density` instead.
 
     Methods
     -------
@@ -177,26 +234,6 @@ class PCA(AnalysisBase):
 
     def __init__(self, universe, select='all', align=False, mean=None,
                  n_components=None, **kwargs):
-        """
-        Parameters
-        ----------
-        universe : Universe
-            Universe
-        select : string, optional
-            A valid selection statement for choosing a subset of atoms from
-            the atomgroup.
-        align : boolean, optional
-            If True, the trajectory will be aligned to a reference
-            structure.
-        mean : MDAnalysis atomgroup, optional
-            An optional reference structure to be used as the mean of the
-            covariance matrix.
-        n_components : int, optional
-            The number of principal components to be saved, default saves
-            all principal components
-        verbose : bool (optional)
-             Show detailed progress of the calculation if set to ``True``.
-        """
         super(PCA, self).__init__(universe.trajectory, **kwargs)
         self._u = universe
 
@@ -246,8 +283,7 @@ def _prepare(self):
                 self.mean += self._atoms.positions.ravel()
             self.mean /= self.n_frames
 
-        self.mean_atoms = self._atoms
-        self.mean_atoms.positions = self._atoms.positions
+        self.results.mean_atoms = self._atoms
 
     def _single_frame(self):
         if self.align:
@@ -273,6 +309,38 @@ def _conclude(self):
         self._calculated = True
         self.n_components = self._n_components
 
+    @property
+    def p_components(self):
+        wmsg = ("The `p_components` attribute was deprecated in "
+                "MDAnalysis 2.0.0 and will be removed in MDAnalysis 3.0.0. "
+                "Please use `results.p_components` instead.")
+        warnings.warn(wmsg, DeprecationWarning)
+        return self.results.p_components
+
+    @property
+    def variance(self):
+        wmsg = ("The `variance` attribute was deprecated in "
+                "MDAnalysis 2.0.0 and will be removed in MDAnalysis 3.0.0. "
+                "Please use `results.variance` instead.")
+        warnings.warn(wmsg, DeprecationWarning)
+        return self.results.variance
+
+    @property
+    def cumulated_variance(self):
+        wmsg = ("The `cumulated_variance` attribute was deprecated in "
+                "MDAnalysis 2.0.0 and will be removed in MDAnalysis 3.0.0. "
+                "Please use `results.cumulated_variance` instead.")
+        warnings.warn(wmsg, DeprecationWarning)
+        return self.results.cumulated_variance
+
+    @property
+    def mean_atoms(self):
+        wmsg = ("The `mean_atoms` attribute was deprecated in "
+                "MDAnalysis 2.0.0 and will be removed in MDAnalysis 3.0.0. "
+                "Please use `results.mean_atoms` instead.")
+        warnings.warn(wmsg, DeprecationWarning)
+        return self.results.mean_atoms
+
     @property
     def n_components(self):
         return self._n_components
@@ -282,10 +350,10 @@ def n_components(self, n):
         if self._calculated:
             if n is None:
                 n = len(self._variance)
-            self.variance = self._variance[:n]
-            self.cumulated_variance = (np.cumsum(self._variance) /
+            self.results.variance = self._variance[:n]
+            self.results.cumulated_variance = (np.cumsum(self._variance) /
                                        np.sum(self._variance))[:n]
-            self.p_components = self._p_components[:, :n]
+            self.results.p_components = self._p_components[:, :n]
         self._n_components = n
 
     def transform(self, atomgroup, n_components=None, start=None, stop=None,
@@ -339,7 +407,7 @@ def transform(self, atomgroup, n_components=None, start=None, stop=None,
         n_frames = len(range(start, stop, step))
 
         dim = (n_components if n_components is not None else
-               self.p_components.shape[1])
+               self.results.p_components.shape[1])
 
         dot = np.zeros((n_frames, dim))
 
@@ -386,7 +454,7 @@ def rmsip(self, other, n_components=None):
         .. versionadded:: 1.0.0
         """
         try:
-            a = self.p_components
+            a = self.results.p_components
         except AttributeError:
             raise ValueError('Call run() on the PCA before using rmsip')
 
@@ -440,7 +508,7 @@ def cumulative_overlap(self, other, i=0, n_components=None):
         """
 
         try:
-            a = self.p_components
+            a = self.results.p_components
         except AttributeError:
             raise ValueError(
                 'Call run() on the PCA before using cumulative_overlap')
diff --git a/testsuite/MDAnalysisTests/analysis/test_pca.py b/testsuite/MDAnalysisTests/analysis/test_pca.py
index 18c47d649f9..0c7cdaa4ef1 100644
--- a/testsuite/MDAnalysisTests/analysis/test_pca.py
+++ b/testsuite/MDAnalysisTests/analysis/test_pca.py
@@ -69,23 +69,23 @@ def test_cov(pca, u):
 
 
 def test_cum_var(pca):
-    assert_almost_equal(pca.cumulated_variance[-1], 1)
-    l = pca.cumulated_variance
+    assert_almost_equal(pca.results.cumulated_variance[-1], 1)
+    l = pca.results.cumulated_variance
     l = np.sort(l)
-    assert_almost_equal(pca.cumulated_variance, l, 5)
+    assert_almost_equal(pca.results.cumulated_variance, l, 5)
 
 
 def test_pcs(pca):
-    assert_equal(pca.p_components.shape, (pca._n_atoms * 3, pca._n_atoms * 3))
+    assert_equal(pca.results.p_components.shape, (pca._n_atoms * 3, pca._n_atoms * 3))
 
 
 def test_pcs_n_components(u):
     pca = PCA(u, select=SELECTION).run()
     assert_equal(pca.n_components, pca._n_atoms*3)
-    assert_equal(pca.p_components.shape, (pca._n_atoms * 3, pca._n_atoms * 3))
+    assert_equal(pca.results.p_components.shape, (pca._n_atoms * 3, pca._n_atoms * 3))
     pca.n_components = 10
     assert_equal(pca.n_components, 10)
-    assert_equal(pca.p_components.shape, (pca._n_atoms * 3, 10))
+    assert_equal(pca.results.p_components.shape, (pca._n_atoms * 3, 10))
 
 
 def test_different_steps(pca, u):
@@ -182,8 +182,8 @@ def test_pca_rmsip_self(pca):
 
 
 def test_rmsip_ortho(pca):
-    value = rmsip(pca.p_components[:, :10].T,
-                  pca.p_components[:, 10:20].T)
+    value = rmsip(pca.results.p_components[:, :10].T,
+                  pca.results.p_components[:, 10:20].T)
     assert_almost_equal(value, 0.0)
 
 
@@ -207,7 +207,7 @@ def test_pca_cumulative_overlap_self(pca):
 
 
 def test_cumulative_overlap_ortho(pca):
-    pcs = pca.p_components
+    pcs = pca.results.p_components
     value = cumulative_overlap(pcs[:, 11].T, pcs.T, n_components=10)
     assert_almost_equal(value, 0.0)
 
@@ -241,4 +241,4 @@ def test_compare_wrong_class(u, pca, method):
     func = getattr(pca, method)
     with pytest.raises(ValueError) as exc:
         func(3)
-    assert 'must be another PCA class' in str(exc.value)
+    assert 'must be another PCA class' in str(exc.value)
\ No newline at end of file

From 2afaa09acbb22590234c1618fc25e55e25ba3f18 Mon Sep 17 00:00:00 2001
From: Philip Loche <ploche@physik.fu-berlin.de>
Date: Fri, 7 May 2021 12:37:44 +0200
Subject: [PATCH 02/17] Added tests

---
 .../MDAnalysisTests/analysis/test_pca.py      | 22 ++++++++++++++-----
 1 file changed, 16 insertions(+), 6 deletions(-)

diff --git a/testsuite/MDAnalysisTests/analysis/test_pca.py b/testsuite/MDAnalysisTests/analysis/test_pca.py
index 0c7cdaa4ef1..4a1f3cfb3c5 100644
--- a/testsuite/MDAnalysisTests/analysis/test_pca.py
+++ b/testsuite/MDAnalysisTests/analysis/test_pca.py
@@ -70,19 +70,21 @@ def test_cov(pca, u):
 
 def test_cum_var(pca):
     assert_almost_equal(pca.results.cumulated_variance[-1], 1)
-    l = pca.results.cumulated_variance
-    l = np.sort(l)
-    assert_almost_equal(pca.results.cumulated_variance, l, 5)
+    cum_var = pca.results.cumulated_variance
+    cum_var = np.sort(l)
+    assert_almost_equal(pca.results.cumulated_variance, cum_var, 5)
 
 
 def test_pcs(pca):
-    assert_equal(pca.results.p_components.shape, (pca._n_atoms * 3, pca._n_atoms * 3))
+    assert_equal(pca.results.p_components.shape, (pca._n_atoms * 3, 
+                                                  pca._n_atoms * 3))
 
 
 def test_pcs_n_components(u):
     pca = PCA(u, select=SELECTION).run()
     assert_equal(pca.n_components, pca._n_atoms*3)
-    assert_equal(pca.results.p_components.shape, (pca._n_atoms * 3, pca._n_atoms * 3))
+    assert_equal(pca.results.p_components.shape, (pca._n_atoms * 3, 
+                                                  pca._n_atoms * 3))
     pca.n_components = 10
     assert_equal(pca.n_components, 10)
     assert_equal(pca.results.p_components.shape, (pca._n_atoms * 3, 10))
@@ -241,4 +243,12 @@ def test_compare_wrong_class(u, pca, method):
     func = getattr(pca, method)
     with pytest.raises(ValueError) as exc:
         func(3)
-    assert 'must be another PCA class' in str(exc.value)
\ No newline at end of file
+    assert 'must be another PCA class' in str(exc.value)
+
+@pytest.mark.parametrize("attr", ("p_components", "variance", 
+                                  "cumulated_variance", "mean_atoms"))
+def test_pca_attr_warning(u, attr):
+    pca = PCA(u, select=SELECTION).run(stop=2)
+    wmsg = f"The `{attr}` attribute was deprecated in MDAnalysis 2.0.0"
+    with pytest.warns(DeprecationWarning, match=wmsg):
+        assert_equal(getattr(pca, attr), pca.results[attr])

From ce4a17ec0af1679f97333b0ece4ed17da59162f1 Mon Sep 17 00:00:00 2001
From: Philip Loche <ploche@physik.fu-berlin.de>
Date: Fri, 7 May 2021 12:48:34 +0200
Subject: [PATCH 03/17] Updated CHANGELOG

---
 package/CHANGELOG | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/package/CHANGELOG b/package/CHANGELOG
index 35569ec940e..eeec43e3e6b 100644
--- a/package/CHANGELOG
+++ b/package/CHANGELOG
@@ -174,6 +174,9 @@ Enhancements
     checking if it can be used in parallel analysis. (Issue #2996, PR #2950)
 
 Changes
+  * `analysis.pca.PCA` class now stores `p_components`, `variance`, 
+    `cumulated_variance` and `mean_atoms` using the 
+    `analysis.base.Results` class (Issues #3275 #3285)
   * `analysis.rms.RMSD` and `analysis.rms.RMSF` classes now store `rmsd` and
     `rmsf` data using the `analysis.base.Results` class (Issues #3274 #3261)
   * `analysis.dihedrals` classes now store angle data using the
@@ -235,6 +238,11 @@ Changes
   * Added OpenMM coordinate and topology converters (Issue #2863, PR #2917)
 
 Deprecations
+  * The attributes `p_components`, `variance`, `cumulated_variance` and 
+    `mean_atoms` in `analysis.pca.PCA` are now deprecated in favour of 
+    `results.p_components`, `results.variance`, `results.cumulated_variance` 
+    and `results.mean_atoms`. They will be removed in 3.0.0 
+    (Issues #3275 #3285)
   * The `analysis.rms.RMSD.rmsd` and `analysis.rms.RMSF.rmsf` attributes are
     now deprecated in favour of `analysis.rms.RMSD.results.rmsd` and
     `analysis.rms.RMSF.results.rmsf` respectively. They will be removed in

From c2f16d266a3df5263950f872fb80a0be1cd43d85 Mon Sep 17 00:00:00 2001
From: Philip Loche <ploche@physik.fu-berlin.de>
Date: Fri, 7 May 2021 14:09:51 +0200
Subject: [PATCH 04/17] Docs updated

---
 package/MDAnalysis/analysis/pca.py | 31 ++++++++++++++++++++++--------
 1 file changed, 23 insertions(+), 8 deletions(-)

diff --git a/package/MDAnalysis/analysis/pca.py b/package/MDAnalysis/analysis/pca.py
index 88e69cdfd72..de962855975 100644
--- a/package/MDAnalysis/analysis/pca.py
+++ b/package/MDAnalysis/analysis/pca.py
@@ -70,6 +70,7 @@
 First load all modules and test data
 
 .. code-block:: python
+
     import MDAnalysis as mda
     import MDAnalysis.analysis.pca as pca
     rom MDAnalysis.tests.datafiles import PSF, DCD
@@ -80,6 +81,7 @@
 components.
 
 .. code-block:: python
+
     u = mda.Universe(PSF, DCD)
     PSF_pca = pca.PCA(u, select='backbone')
     PSF_pca.run()
@@ -93,6 +95,7 @@
 is the sum of the variances from 0 to i.
 
 .. code-block:: python
+
     n_pcs = np.where(PSF_pca.cumulated_variance > 0.95)[0][0]
     atomgroup = u.select_atoms('backbone')
     pca_space = PSF_pca.transform(atomgroup, n_components=n_pcs)
@@ -130,6 +133,7 @@ class PCA(AnalysisBase):
     variance will be available for analysis. As an example:
 
     .. code-block:: python
+
         pca = PCA(universe, select='backbone').run()
         pca_space = pca.transform(universe.select_atoms('backbone'), 3)
 
@@ -166,23 +170,27 @@ class PCA(AnalysisBase):
         The column vector p_components[:, i] is the eigenvector
         corresponding to the variance[i].
 
+        .. versionadded:: 2.0.0
+
     p_components: array, (n_atoms * 3, n_components)
         Alias to the :attr:`results.p_components`.
 
         .. deprecated:: 2.0.0
                 Will be removed in MDAnalysis 3.0.0. Please use
-                :attr:`results.density` instead.
+                :attr:`results.p_components` instead.
 
     results.variance : array (n_components, )
         Raw variance explained by each eigenvector of the covariance
         matrix.
 
+        .. versionadded:: 2.0.0
+
     variance : array (n_components, )
         Alias to the :attr:`results.variance`.
 
         .. deprecated:: 2.0.0
                 Will be removed in MDAnalysis 3.0.0. Please use
-                :attr:`results.density` instead.
+                :attr:`results.variance` instead.
 
     results.cumulated_variance : array, (n_components, )
         Percentage of variance explained by the selected components and the sum
@@ -190,22 +198,26 @@ class PCA(AnalysisBase):
         then all components are stored and the cumulated variance will converge
         to 1.
 
+        .. versionadded:: 2.0.0
+
     cumulated_variance : array, (n_components, )
         Alias to the :attr:`results.cumulated_variance`.
 
         .. deprecated:: 2.0.0
                 Will be removed in MDAnalysis 3.0.0. Please use
-                :attr:`results.density` instead.
+                :attr:`results.cumulated_variance` instead.
 
     results.mean_atoms: MDAnalyis atomgroup
         Atoms used for the creation of the covariance matrix.
 
+        .. versionadded:: 2.0.0
+
     mean_atoms: MDAnalyis atomgroup
         Alias to the :attr:`results.mean_atoms`.
 
         .. deprecated:: 2.0.0
                 Will be removed in MDAnalysis 3.0.0. Please use
-                :attr:`results.density` instead.
+                :attr:`results.mean_atoms` instead.
 
     Methods
     -------
@@ -218,6 +230,9 @@ class PCA(AnalysisBase):
     -----
     Computation can be sped up by supplying a precalculated mean structure.
 
+    .. versionchanged:: 0.19.0
+       The start frame is used when performing selections and calculating
+       mean positions.  Previously the 0th frame was always used.
     .. versionchanged:: 1.0.0
        ``n_components`` now limits the correct axis of ``p_components``.
        ``cumulated_variance`` now accurately represents the contribution of
@@ -226,10 +241,10 @@ class PCA(AnalysisBase):
        ``p_components``, ``cumulated_variance`` will not sum to 1.
        ``align=True`` now correctly aligns the trajectory and computes the
        correct means and covariance matrix.
-
-    .. versionchanged:: 0.19.0
-       The start frame is used when performing selections and calculating
-       mean positions.  Previously the 0th frame was always used.
+    .. versionchanged:: 2.0.0
+        :attr:`p_components`, :attr:`variance`, :attr:`cumulated_variance`
+        and :attr:`mean_atoms` are now stored in a
+        :class:`MDAnalysis.analysis.base.Results` instance.
     """
 
     def __init__(self, universe, select='all', align=False, mean=None,

From 813af65e6943bbda0790e230b7cf1126093a8b2f Mon Sep 17 00:00:00 2001
From: Philip Loche <ploche@physik.fu-berlin.de>
Date: Fri, 7 May 2021 14:44:05 +0200
Subject: [PATCH 05/17] PEP8

---
 testsuite/MDAnalysisTests/analysis/test_pca.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/testsuite/MDAnalysisTests/analysis/test_pca.py b/testsuite/MDAnalysisTests/analysis/test_pca.py
index 4a1f3cfb3c5..93b9bd25a98 100644
--- a/testsuite/MDAnalysisTests/analysis/test_pca.py
+++ b/testsuite/MDAnalysisTests/analysis/test_pca.py
@@ -76,14 +76,14 @@ def test_cum_var(pca):
 
 
 def test_pcs(pca):
-    assert_equal(pca.results.p_components.shape, (pca._n_atoms * 3, 
+    assert_equal(pca.results.p_components.shape, (pca._n_atoms * 3,
                                                   pca._n_atoms * 3))
 
 
 def test_pcs_n_components(u):
     pca = PCA(u, select=SELECTION).run()
     assert_equal(pca.n_components, pca._n_atoms*3)
-    assert_equal(pca.results.p_components.shape, (pca._n_atoms * 3, 
+    assert_equal(pca.results.p_components.shape, (pca._n_atoms * 3,
                                                   pca._n_atoms * 3))
     pca.n_components = 10
     assert_equal(pca.n_components, 10)
@@ -245,7 +245,8 @@ def test_compare_wrong_class(u, pca, method):
         func(3)
     assert 'must be another PCA class' in str(exc.value)
 
-@pytest.mark.parametrize("attr", ("p_components", "variance", 
+
+@pytest.mark.parametrize("attr", ("p_components", "variance",
                                   "cumulated_variance", "mean_atoms"))
 def test_pca_attr_warning(u, attr):
     pca = PCA(u, select=SELECTION).run(stop=2)

From c915fa6ff874931911e3c049286a188bb41f37e7 Mon Sep 17 00:00:00 2001
From: Philip Loche <ploche@physik.fu-berlin.de>
Date: Fri, 7 May 2021 14:46:01 +0200
Subject: [PATCH 06/17] Fixed forgotten rename

---
 testsuite/MDAnalysisTests/analysis/test_pca.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/testsuite/MDAnalysisTests/analysis/test_pca.py b/testsuite/MDAnalysisTests/analysis/test_pca.py
index 93b9bd25a98..7dadce48af2 100644
--- a/testsuite/MDAnalysisTests/analysis/test_pca.py
+++ b/testsuite/MDAnalysisTests/analysis/test_pca.py
@@ -71,7 +71,7 @@ def test_cov(pca, u):
 def test_cum_var(pca):
     assert_almost_equal(pca.results.cumulated_variance[-1], 1)
     cum_var = pca.results.cumulated_variance
-    cum_var = np.sort(l)
+    cum_var = np.sort(cum_var)
     assert_almost_equal(pca.results.cumulated_variance, cum_var, 5)
 
 

From 2f4c85809d874b0b02d001d589706c98eab42ba8 Mon Sep 17 00:00:00 2001
From: Philip Loche <ploche@physik.fu-berlin.de>
Date: Sat, 8 May 2021 00:23:52 +0200
Subject: [PATCH 07/17] check object instance

---
 testsuite/MDAnalysisTests/analysis/test_pca.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/testsuite/MDAnalysisTests/analysis/test_pca.py b/testsuite/MDAnalysisTests/analysis/test_pca.py
index 7dadce48af2..00b46d07718 100644
--- a/testsuite/MDAnalysisTests/analysis/test_pca.py
+++ b/testsuite/MDAnalysisTests/analysis/test_pca.py
@@ -252,4 +252,4 @@ def test_pca_attr_warning(u, attr):
     pca = PCA(u, select=SELECTION).run(stop=2)
     wmsg = f"The `{attr}` attribute was deprecated in MDAnalysis 2.0.0"
     with pytest.warns(DeprecationWarning, match=wmsg):
-        assert_equal(getattr(pca, attr), pca.results[attr])
+        getattr(pca, attr) is pca.results[attr]

From 45c238efa56df8f0ea91e614b16073610253dd9a Mon Sep 17 00:00:00 2001
From: Philip Loche <ploche@physik.fu-berlin.de>
Date: Sat, 8 May 2021 01:05:12 +0200
Subject: [PATCH 08/17] Update package/MDAnalysis/analysis/pca.py

Co-authored-by: Irfan Alibay <IAlibay@users.noreply.github.com>
---
 package/MDAnalysis/analysis/pca.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/package/MDAnalysis/analysis/pca.py b/package/MDAnalysis/analysis/pca.py
index de962855975..190a641031e 100644
--- a/package/MDAnalysis/analysis/pca.py
+++ b/package/MDAnalysis/analysis/pca.py
@@ -91,7 +91,7 @@
 to retain. The choice is arbitrary, but I will stop when 95 percent of the
 variance is explained by the components. This cumulated variance by the
 components is conveniently stored in the one-dimensional array attribute
-``cumulated_variance``. The value at the ith index of `cumulated_variance`
+``results.cumulated_variance``. The value at the ith index of `results.cumulated_variance`
 is the sum of the variances from 0 to i.
 
 .. code-block:: python

From 91941165dd9f175f34ba502a245d56c38404cedb Mon Sep 17 00:00:00 2001
From: Philip Loche <ploche@physik.fu-berlin.de>
Date: Sat, 8 May 2021 01:07:52 +0200
Subject: [PATCH 09/17] PEP8

---
 package/MDAnalysis/analysis/pca.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/package/MDAnalysis/analysis/pca.py b/package/MDAnalysis/analysis/pca.py
index 190a641031e..31705cb2781 100644
--- a/package/MDAnalysis/analysis/pca.py
+++ b/package/MDAnalysis/analysis/pca.py
@@ -91,8 +91,8 @@
 to retain. The choice is arbitrary, but I will stop when 95 percent of the
 variance is explained by the components. This cumulated variance by the
 components is conveniently stored in the one-dimensional array attribute
-``results.cumulated_variance``. The value at the ith index of `results.cumulated_variance`
-is the sum of the variances from 0 to i.
+``results.cumulated_variance``. The value at the ith index of
+``results.cumulated_variance`` is the sum of the variances from 0 to i.
 
 .. code-block:: python
 

From 8320edd7e7c3483d228351624b30045f11015b45 Mon Sep 17 00:00:00 2001
From: Philip Loche <ploche@physik.fu-berlin.de>
Date: Mon, 10 May 2021 11:08:32 +0200
Subject: [PATCH 10/17] Apply suggestions from fiona

Co-authored-by: Fiona Naughton <fiona.naughton@asu.edu>
---
 package/MDAnalysis/analysis/pca.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/package/MDAnalysis/analysis/pca.py b/package/MDAnalysis/analysis/pca.py
index 31705cb2781..93caa5c4152 100644
--- a/package/MDAnalysis/analysis/pca.py
+++ b/package/MDAnalysis/analysis/pca.py
@@ -73,7 +73,7 @@
 
     import MDAnalysis as mda
     import MDAnalysis.analysis.pca as pca
-    rom MDAnalysis.tests.datafiles import PSF, DCD
+    from MDAnalysis.tests.datafiles import PSF, DCD
 
 
 Given a universe containing trajectory data we can perform Principal Component
@@ -96,7 +96,7 @@
 
 .. code-block:: python
 
-    n_pcs = np.where(PSF_pca.cumulated_variance > 0.95)[0][0]
+    n_pcs = np.where(PSF_pca.results.cumulated_variance > 0.95)[0][0]
     atomgroup = u.select_atoms('backbone')
     pca_space = PSF_pca.transform(atomgroup, n_components=n_pcs)
 

From d84380125926d8618a60da8af623c5bc032bc176 Mon Sep 17 00:00:00 2001
From: Oliver Beckstein <orbeckst@gmail.com>
Date: Wed, 26 May 2021 15:48:01 -0700
Subject: [PATCH 11/17] removed deprecation test for mean_atoms

---
 testsuite/MDAnalysisTests/analysis/test_pca.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/testsuite/MDAnalysisTests/analysis/test_pca.py b/testsuite/MDAnalysisTests/analysis/test_pca.py
index 44fd2ef4f30..931a04673f8 100644
--- a/testsuite/MDAnalysisTests/analysis/test_pca.py
+++ b/testsuite/MDAnalysisTests/analysis/test_pca.py
@@ -256,7 +256,7 @@ def test_compare_wrong_class(u, pca, method):
 
 
 @pytest.mark.parametrize("attr", ("p_components", "variance",
-                                  "cumulated_variance", "mean_atoms"))
+                                  "cumulated_variance"))
 def test_pca_attr_warning(u, attr):
     pca = PCA(u, select=SELECTION).run(stop=2)
     wmsg = f"The `{attr}` attribute was deprecated in MDAnalysis 2.0.0"

From 3dfdb1553cf34bc1abe9c4036e6f70741594868e Mon Sep 17 00:00:00 2001
From: Philip Loche <ploche@physik.fu-berlin.de>
Date: Thu, 27 May 2021 11:26:05 +0200
Subject: [PATCH 12/17] Smooth and tidy docs

---
 package/MDAnalysis/analysis/pca.py | 33 +++++++++++++-----------------
 1 file changed, 14 insertions(+), 19 deletions(-)

diff --git a/package/MDAnalysis/analysis/pca.py b/package/MDAnalysis/analysis/pca.py
index 860bb88a3a0..02c85c29cfe 100644
--- a/package/MDAnalysis/analysis/pca.py
+++ b/package/MDAnalysis/analysis/pca.py
@@ -46,7 +46,8 @@
 explains. Stored in :attr:`PCA.cumulated_variance`, a ratio for each number of
 eigenvectors up to index :math:`i` is provided to quickly find out how many
 principal components are needed to explain the amount of variance reflected by
-those :math:`i` eigenvectors. For most data, :attr:`PCA.cumulated_variance`
+those :math:`i` eigenvectors. For most data, 
+:attr:`PCA.results.cumulated_variance`
 will be approximately equal to one for some :math:`n` that is significantly
 smaller than the total number of components, these are the components of
 interest given by Principal Component Analysis.
@@ -67,9 +68,7 @@
 :data:`~MDAnalysis.tests.datafiles.DCD`). This tutorial shows how to use the
 PCA class.
 
-First load all modules and test data
-
-.. code-block:: python
+First load all modules and test data::
 
     import MDAnalysis as mda
     import MDAnalysis.analysis.pca as pca
@@ -78,9 +77,7 @@
 
 Given a universe containing trajectory data we can perform Principal Component
 Analyis by using the class :class:`PCA` and retrieving the principal
-components.
-
-.. code-block:: python
+components.::
 
     u = mda.Universe(PSF, DCD)
     PSF_pca = pca.PCA(u, select='backbone')
@@ -91,10 +88,9 @@
 to retain. The choice is arbitrary, but I will stop when 95 percent of the
 variance is explained by the components. This cumulated variance by the
 components is conveniently stored in the one-dimensional array attribute
-``results.cumulated_variance``. The value at the ith index of
-``results.cumulated_variance`` is the sum of the variances from 0 to i.
-
-.. code-block:: python
+:attr:`PCA.results.cumulated_variance`. The value at the ith index of
+:attr:`PCA.results.cumulated_variance` is the sum of the variances from 0 to 
+i.::
 
     n_pcs = np.where(PSF_pca.results.cumulated_variance > 0.95)[0][0]
     atomgroup = u.select_atoms('backbone')
@@ -133,9 +129,7 @@ class PCA(AnalysisBase):
 
     After initializing and calling method with a universe or an atom group,
     principal components ordering the atom coordinate data by decreasing
-    variance will be available for analysis. As an example:
-
-    .. code-block:: python
+    variance will be available for analysis. As an example:::
 
         pca = PCA(universe, select='backbone').run()
         pca_space = pca.transform(universe.select_atoms('backbone'), 3)
@@ -158,7 +152,7 @@ class PCA(AnalysisBase):
         structure.
     mean : array_like, optional
         Optional reference positions to be be used as the mean of the
-        covariance matrix.        
+        covariance matrix.
     n_components : int, optional
         The number of principal components to be saved, default saves
         all principal components
@@ -237,7 +231,7 @@ class PCA(AnalysisBase):
     .. versionchanged:: 2.0.0
        ``mean_atoms`` removed, as this did not reliably contain the mean
        positions.
-       ``mean`` input now accepts coordinate arrays instead of atomgroup.    
+       ``mean`` input now accepts coordinate arrays instead of atomgroup.
        :attr:`p_components`, :attr:`variance` and :attr:`cumulated_variance`
        are now stored in a :class:`MDAnalysis.analysis.base.Results` instance.
     """
@@ -456,7 +450,8 @@ def rmsip(self, other, n_components=None):
 
         See also
         --------
-        rmsip
+        :func:`rmsip`
+
 
         .. versionadded:: 1.0.0
         """
@@ -508,10 +503,10 @@ def cumulative_overlap(self, other, i=0, n_components=None):
 
         See also
         --------
-        cumulative_overlap
+        :func:`cumulative_overlap`
 
-        .. versionadded:: 1.0.0
 
+        .. versionadded:: 1.0.0
         """
 
         try:

From 72a2bb0aba0a3b943e7c766bc0d114f4d2d269d8 Mon Sep 17 00:00:00 2001
From: Oliver Beckstein <orbeckst@gmail.com>
Date: Thu, 27 May 2021 09:17:50 -0700
Subject: [PATCH 13/17] Apply suggestions from code review

---
 package/MDAnalysis/analysis/pca.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/package/MDAnalysis/analysis/pca.py b/package/MDAnalysis/analysis/pca.py
index 02c85c29cfe..fe61f12ff50 100644
--- a/package/MDAnalysis/analysis/pca.py
+++ b/package/MDAnalysis/analysis/pca.py
@@ -450,7 +450,7 @@ def rmsip(self, other, n_components=None):
 
         See also
         --------
-        :func:`rmsip`
+        :func:`~MDAnalysis.analysis.pca.rmsip`
 
 
         .. versionadded:: 1.0.0
@@ -503,7 +503,7 @@ def cumulative_overlap(self, other, i=0, n_components=None):
 
         See also
         --------
-        :func:`cumulative_overlap`
+        :func:`~MDAnalysis.analysis.pca.cumulative_overlap`
 
 
         .. versionadded:: 1.0.0

From 6fadd78e20bd9d3ec8d885d587c396acc60b6c93 Mon Sep 17 00:00:00 2001
From: Oliver Beckstein <orbeckst@gmail.com>
Date: Thu, 27 May 2021 09:52:39 -0700
Subject: [PATCH 14/17] doc fix

thanks @IAlibay
---
 package/MDAnalysis/analysis/pca.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/package/MDAnalysis/analysis/pca.py b/package/MDAnalysis/analysis/pca.py
index fe61f12ff50..f5450f407f5 100644
--- a/package/MDAnalysis/analysis/pca.py
+++ b/package/MDAnalysis/analysis/pca.py
@@ -43,13 +43,13 @@
 components are the eigenvectors of this matrix.
 
 For each eigenvector, its eigenvalue is the variance that the eigenvector
-explains. Stored in :attr:`PCA.cumulated_variance`, a ratio for each number of
-eigenvectors up to index :math:`i` is provided to quickly find out how many
-principal components are needed to explain the amount of variance reflected by
-those :math:`i` eigenvectors. For most data, 
+explains. Stored in :attr:`PCA.results.cumulated_variance`, a ratio for each 
+number of eigenvectors up to index :math:`i` is provided to quickly find out 
+how many principal components are needed to explain the amount of variance 
+reflected by those :math:`i` eigenvectors. For most data, 
 :attr:`PCA.results.cumulated_variance`
 will be approximately equal to one for some :math:`n` that is significantly
-smaller than the total number of components, these are the components of
+smaller than the total number of components. These are the components of
 interest given by Principal Component Analysis.
 
 From here, we can project a trajectory onto these principal components and

From 9b7f6a6ac70a9cf6c592e48d20875f47214165b7 Mon Sep 17 00:00:00 2001
From: Oliver Beckstein <orbeckst@gmail.com>
Date: Thu, 27 May 2021 09:56:13 -0700
Subject: [PATCH 15/17] pep8 trailing whitespace fixes

---
 package/MDAnalysis/analysis/pca.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/package/MDAnalysis/analysis/pca.py b/package/MDAnalysis/analysis/pca.py
index f5450f407f5..f1a3e442944 100644
--- a/package/MDAnalysis/analysis/pca.py
+++ b/package/MDAnalysis/analysis/pca.py
@@ -43,10 +43,10 @@
 components are the eigenvectors of this matrix.
 
 For each eigenvector, its eigenvalue is the variance that the eigenvector
-explains. Stored in :attr:`PCA.results.cumulated_variance`, a ratio for each 
-number of eigenvectors up to index :math:`i` is provided to quickly find out 
-how many principal components are needed to explain the amount of variance 
-reflected by those :math:`i` eigenvectors. For most data, 
+explains. Stored in :attr:`PCA.results.cumulated_variance`, a ratio for each
+number of eigenvectors up to index :math:`i` is provided to quickly find out
+how many principal components are needed to explain the amount of variance
+reflected by those :math:`i` eigenvectors. For most data,
 :attr:`PCA.results.cumulated_variance`
 will be approximately equal to one for some :math:`n` that is significantly
 smaller than the total number of components. These are the components of
@@ -89,7 +89,7 @@
 variance is explained by the components. This cumulated variance by the
 components is conveniently stored in the one-dimensional array attribute
 :attr:`PCA.results.cumulated_variance`. The value at the ith index of
-:attr:`PCA.results.cumulated_variance` is the sum of the variances from 0 to 
+:attr:`PCA.results.cumulated_variance` is the sum of the variances from 0 to
 i.::
 
     n_pcs = np.where(PSF_pca.results.cumulated_variance > 0.95)[0][0]

From ef1c00dc7c4824352e5aeaebdd65421061fa8bd4 Mon Sep 17 00:00:00 2001
From: Oliver Beckstein <orbeckst@gmail.com>
Date: Thu, 27 May 2021 11:05:46 -0700
Subject: [PATCH 16/17] cross-referenced rmsip <-> cumulative_overlap

---
 package/MDAnalysis/analysis/pca.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/package/MDAnalysis/analysis/pca.py b/package/MDAnalysis/analysis/pca.py
index f1a3e442944..bebc9038e3b 100644
--- a/package/MDAnalysis/analysis/pca.py
+++ b/package/MDAnalysis/analysis/pca.py
@@ -450,7 +450,7 @@ def rmsip(self, other, n_components=None):
 
         See also
         --------
-        :func:`~MDAnalysis.analysis.pca.rmsip`
+        :meth:`cumulative_overlap`
 
 
         .. versionadded:: 1.0.0
@@ -503,7 +503,7 @@ def cumulative_overlap(self, other, i=0, n_components=None):
 
         See also
         --------
-        :func:`~MDAnalysis.analysis.pca.cumulative_overlap`
+        :meth:`rmsip`
 
 
         .. versionadded:: 1.0.0

From a2f52877adb84f1ccb77075883bfd3ef927237e2 Mon Sep 17 00:00:00 2001
From: Oliver Beckstein <orbeckst@gmail.com>
Date: Thu, 27 May 2021 12:00:04 -0700
Subject: [PATCH 17/17] fixed docs, for good

---
 package/MDAnalysis/analysis/pca.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/package/MDAnalysis/analysis/pca.py b/package/MDAnalysis/analysis/pca.py
index bebc9038e3b..c12d79e347c 100644
--- a/package/MDAnalysis/analysis/pca.py
+++ b/package/MDAnalysis/analysis/pca.py
@@ -109,6 +109,10 @@
 
 .. autofunction:: cosine_content
 
+.. autofunction:: rmsip
+
+.. autofunction:: cumulative_overlap
+
 """
 import warnings
 
@@ -450,7 +454,7 @@ def rmsip(self, other, n_components=None):
 
         See also
         --------
-        :meth:`cumulative_overlap`
+        :func:`~MDAnalysis.analysis.pca.rmsip`
 
 
         .. versionadded:: 1.0.0
@@ -503,7 +507,7 @@ def cumulative_overlap(self, other, i=0, n_components=None):
 
         See also
         --------
-        :meth:`rmsip`
+        :func:`~MDAnalysis.analysis.pca.cumulative_overlap`
 
 
         .. versionadded:: 1.0.0
@@ -594,6 +598,7 @@ def rmsip(a, b, n_components=None):
         0 indicates that they are mutually orthogonal, whereas 1 indicates
         that they are identical.
 
+
     .. versionadded:: 1.0.0
     """
     n_components = util.asiterable(n_components)
@@ -647,6 +652,7 @@ def cumulative_overlap(a, b, i=0, n_components=None):
         0 indicates that they are mutually orthogonal, whereas 1 indicates
         that they are identical.
 
+
     .. versionadded:: 1.0.0
     """