Merge pull request #891 from jdetle/nframes

Changed nframes to n_frames, fixes #890

package/CHANGELOG

@@ -13,7 +13,7 @@ The rules for this file:
   * release numbers follow "Semantic Versioning" http://semver.org
 
 ------------------------------------------------------------------------------
-??/??/16 kain88-de, jdetle, fiona-naughton, richardjgowers, tyler.je.reddy
+??/??/16 kain88-de, fiona-naughton, richardjgowers, tyler.je.reddy, jdetle
 
   * 0.15.1
 
@@ -24,7 +24,8 @@ Fixes
   * SingleFrameReader now raises StopIteration instead of IOError on calling
     next (Issue #869)
   * Display of Deprecation warnings doesn't affect other modules anymore (Issue #754)
-
+  * Changed nframes to n_frames in analysis modules for consistency (Issue #890)
+
 Changes
   * Added protected variable _frame_index to to keep track of frame iteration
     number in AnalysisBase

package/MDAnalysis/analysis/align.py

@@ -558,7 +558,7 @@ def _prepare(self):
         self._ref_com = self.ref_atoms.center_of_mass()
         self._ref_coordinates = self.ref_atoms.positions - self._ref_com
         # allocate the array for selection atom coords
-        self.rmsd = np.zeros((self.nframes,))
+        self.rmsd = np.zeros((self.n_frames,))
 
     def _single_frame(self):
         index = self._ts.frame
@@ -717,8 +717,8 @@ def rms_fit_trj(
     traj_coordinates = traj_atoms.positions.copy()
 
     # RMSD timeseries
-    nframes = len(frames)
-    rmsd = np.zeros((nframes,))
+    n_frames = len(frames)
+    rmsd = np.zeros((n_frames,))
 
     # R: rotation matrix that aligns r-r_com, x~-x~com
     #    (x~: selected coordinates, x: all coordinates)
@@ -727,7 +727,7 @@ def rms_fit_trj(
     R = np.matrix(rot.reshape(3, 3))
 
     percentage = ProgressMeter(
-        nframes,
+        n_frames,
         interval=10,
         quiet=quiet,
         format="Fitted frame %(step)5d/%(numsteps)d  [%(percentage)5.1f%%]      r")

package/MDAnalysis/analysis/base.py

@@ -71,7 +71,7 @@ def _setup_frames(self, trajectory, start=None,
         self.start = start
         self.stop = stop
         self.step = step
-        self.nframes = len(range(start, stop, step))
+        self.n_frames = len(range(start, stop, step))
 
     def _single_frame(self):
         """Calculate data from a single frame of trajectory
@@ -99,7 +99,7 @@ def run(self, **kwargs):
                 self._trajectory[self.start:self.stop:self.step]):
             self._frame_index = i
             self._ts = ts
-            # logger.info("--> Doing frame {} of {}".format(i+1, self.nframes))
+            # logger.info("--> Doing frame {} of {}".format(i+1, self.n_frames))
             self._single_frame()
         logger.info("Finishing up")
         self._conclude()

package/MDAnalysis/analysis/lineardensity.py

@@ -1,5 +1,5 @@
 # -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; coding:utf-8 -*-
-# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4 
+# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
 #
 # MDAnalysis --- http://www.MDAnalysis.org
 # Copyright (c) 2006-2015 Naveen Michaud-Agrawal, Elizabeth J. Denning, Oliver Beckstein
@@ -46,7 +46,7 @@ class LinearDensity(AnalysisBase):
 
     binsize : float
           Bin width in Angstrom used to build linear density
-          histograms. Defines the resolution of the resulting density 
+          histograms. Defines the resolution of the resulting density
           profile (smaller --> higher resolution) [0.25]
 
     start : int
@@ -58,7 +58,7 @@ class LinearDensity(AnalysisBase):
 
     Example
     -------
-    First create a LinearDensity object by supplying a selection, 
+    First create a LinearDensity object by supplying a selection,
     then use the `run` method:
       ldens = LinearDensity(selection)
       ldens.run()
@@ -161,7 +161,7 @@ def _conclude(self):
         # Average results over the  number of configurations
         for dim in ['x', 'y', 'z']:
             for key in ['pos', 'pos_std', 'char', 'char_std']:
-                self.results[dim][key] /= self.nframes
+                self.results[dim][key] /= self.n_frames
             # Compute standard deviation for the error
             self.results[dim]['pos_std'] = np.sqrt(self.results[dim]['pos_std']
                                                    - np.square(self.results[dim]['pos']))

package/MDAnalysis/analysis/polymer.py

@@ -1,5 +1,5 @@
 # -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; coding:utf-8 -*-
-# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4 
+# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
 #
 # MDAnalysis --- http://www.MDAnalysis.org
 # Copyright (c) 2006-2015 Naveen Michaud-Agrawal, Elizabeth J. Denning, Oliver Beckstein
@@ -84,7 +84,7 @@ def _single_frame(self):
         # could optimise this by writing a "self dot array"
         # we're only using the upper triangle of np.inner
         # function would accept a bunch of coordinates and spit out the
-        # decorrel for that 
+        # decorrel for that
         n = len(self._atomgroups[0])
 
         for chain in self._atomgroups:
@@ -101,7 +101,7 @@ def _conclude(self):
         n = len(self._atomgroups[0])
 
         norm = np.linspace(n - 1, 1, n - 1)
-        norm *= len(self._atomgroups) * self.nframes
+        norm *= len(self._atomgroups) * self.n_frames
 
         self.results = self._results / norm
         self._calc_bond_length()
@@ -169,5 +169,3 @@ def expfunc(x, a):
     a = curve_fit(expfunc, x, y)[0][0]
 
     return a
-
-

package/MDAnalysis/analysis/rdf.py

@@ -144,9 +144,9 @@ def _conclude(self):
         vol *= 4/3.0 * np.pi
 
         # Average number density
-        box_vol = self.volume / self.nframes
+        box_vol = self.volume / self.n_frames
         density = N / box_vol
 
-        rdf = self.count / (density * vol * self.nframes)
+        rdf = self.count / (density * vol * self.n_frames)
 
         self.rdf = rdf

package/MDAnalysis/analysis/rms.py

@@ -461,11 +461,11 @@ def run(self, start=None, stop=None, step=None,
             rot = None
 
         # RMSD timeseries
-        nframes = len(np.arange(0, len(trajectory))[start:stop:step])
-        rmsd = np.zeros((nframes, 3 + len(self.groupselections_atoms)))
+        n_frames = len(np.arange(0, len(trajectory))[start:stop:step])
+        rmsd = np.zeros((n_frames, 3 + len(self.groupselections_atoms)))
 
         percentage = ProgressMeter(
-            nframes, interval=10, format="RMSD %(rmsd)5.2f A at frame "
+            n_frames, interval=10, format="RMSD %(rmsd)5.2f A at frame "
             "%(step)5d/%(numsteps)d  [%(percentage)5.1f%%]\r")
 
         for k, ts in enumerate(trajectory[start:stop:step]):

testsuite/MDAnalysisTests/analysis/test_base.py

@@ -55,28 +55,28 @@ def tearDown(self):
 
     def test_default(self):
         an = FrameAnalysis(self.u.trajectory)
-        assert_(an.nframes == len(self.u.trajectory))
+        assert_(an.n_frames == len(self.u.trajectory))
 
         an.run()
         assert_(an.frames == list(range(len(self.u.trajectory))))
 
     def test_start(self):
         an = FrameAnalysis(self.u.trajectory, start=20)
-        assert_(an.nframes == len(self.u.trajectory) - 20)
+        assert_(an.n_frames == len(self.u.trajectory) - 20)
 
         an.run()
         assert_(an.frames == list(range(20, len(self.u.trajectory))))
 
     def test_stop(self):
         an = FrameAnalysis(self.u.trajectory, stop=20)
-        assert_(an.nframes == 20)
+        assert_(an.n_frames == 20)
 
         an.run()
         assert_(an.frames == list(range(20)))
 
     def test_step(self):
         an = FrameAnalysis(self.u.trajectory, step=20)
-        assert_(an.nframes == 5)
+        assert_(an.n_frames == 5)
 
         an.run()
         assert_(an.frames == list(range(98))[::20])