Merge pull request #8 from exa-analytics/master

T

Author: tjduigna

.gitignore

@@ -1,10 +1,5 @@
 # Byte-compiled / optimized / DLL files
-__pycache__
-_pycache__*
-__pycache__/*
-atomic/__pycache__/*
-atomic/qe/__pycahce__/*
-atomic/nwchem/__pycache__/*
+__pycache__/
 *.py[cod]
 *.nbc
 *.nbi
@@ -59,7 +54,7 @@ coverage.xml
 *.log
 
 # Sphinx documentation
-doc/build/*
+doc/build/
 
 # PyBuilder
 target/*
@@ -70,3 +65,4 @@ target/*
 .settings/*
 *.swp
 .ipynb*
+*~

.travis.yml

@@ -8,15 +8,16 @@ before_install:
     - ./miniconda.sh -b
     - export PATH=/home/travis/miniconda3/bin:$PATH
     - conda update --yes conda
-    - wget https://github.com/exa-analytics/exa/archive/master.zip
-    - unzip master.zip
 install:
-    - conda install --yes python=$TRAVIS_PYTHON_VERSION atlas numpy scipy matplotlib pandas sqlalchemy networkx sympy seaborn jupyter notebook==4.1 ipywidgets==4.1 ipyparallel sphinx nose
-    - pip install sphinxcontrib-autoanysrc codecov
-    - cd exa-master && pip install .
+    - conda install --yes python=$TRAVIS_PYTHON_VERSION
+    - conda install -c conda-forge --yes jupyter notebook ipywidgets ipyparallel
+    - conda config --add channels https://conda.anaconda.org/exaanalytics
+    - conda install --yes exa
+    - pip install sphinxcontrib-autoanysrc coverage codecov python-coveralls
+    - python setup.py install
     - exa -uu
-    - cd ../exa-master
 script:
     - nosetests --with-doctest --with-coverage
 after_success:
     - codecov
+    - coveralls

README.rst

@@ -1,14 +1,54 @@
 | |logo|
+##################
+Installation
+##################
+| |conda|
+| |pypi|
+Exatomic is available through `anaconda`_,
+
+.. code-block:: bash
+
+    conda install -c exaanalytics exatomic
+
+or `pypi`_.
+
+.. code-block:: bash
+
+    pip install exatomic
+
+###################
+Getting Started
+###################
 | |docs|
-| |gitter|
+| |gitter|
+Documentation can be built using `sphinx`_:
+
+.. code-block:: bash
+
+    cd doc
+    make html    # or .\make.bat html
+
+##################
+Status
+##################
 | |build|
 | |issues|
 | |cov|
+
+###############
+Legal
+###############
 | |lic|
+| Copyright (c) 2015-2016, Exa Analytics Development Team
+| Distributed under the terms of the Apache License 2.0
+
+.. _anaconda: https://www.continuum.io/downloads
+.. _pypi: https://pypi.python.org/pypi
+.. _sphinx: http://www.sphinx-doc.org/en/stable/
 
 .. |logo| image:: doc/source/_static/logo.png
     :target: doc/source/_static/logo.png
-    :alt: Exatomic
+    :alt: Exatomic Analytics
 
 .. |build| image:: https://travis-ci.org/exa-analytics/exatomic.svg?branch=master
     :target: https://travis-ci.org/exa-analytics/exatomic
@@ -18,47 +58,26 @@
     :target: http://exatomic.readthedocs.io/en/latest/?badge=latest
     :alt: Documentation Status
 
-.. |gitter| image:: https://badges.gitter.im/exa-analytics/exatomic.svg
-   :alt: Join the chat at https://gitter.im/exa-analytics/exatomic
-   :target: https://gitter.im/exa-analytics/exatomic?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge
-
+.. |conda| image:: https://anaconda.org/exaanalytics/exatomic/badges/installer/conda.svg
+    :target: https://conda.anaconda.org/exaanalytics
+    :alt: Anaconda Version
+
+.. |pypi| image:: https://badge.fury.io/py/exatomic.svg
+    :target: https://badge.fury.io/py/exatomic
+    :alt: PyPI Version
+
+.. |gitter| image:: https://badges.gitter.im/exa-analytics/exatomic.svg
+   :target: https://gitter.im/exa-analytics/exatomic?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge
+   :alt: Join the chat at https://gitter.im/exa-analytics/exatomic
+
 .. |issues| image:: https://www.quantifiedcode.com/api/v1/project/99e4f26905194100ad4c27aba432ec4c/badge.svg
-    :target: https://www.quantifiedcode.com/app/project/99e4f26905194100ad4c27aba432ec4c
-    :alt: Code issues
+  :target: https://www.quantifiedcode.com/app/project/99e4f26905194100ad4c27aba432ec4c
+  :alt: Code issues
 
-.. |cov| image:: https://codecov.io/gh/exa-analytics/exatomic/branch/master/graph/badge.svg
-    :target: https://codecov.io/gh/exa-analytics/exatomic
+.. |cov| image:: https://coveralls.io/repos/github/exa-analytics/exatomic/badge.svg
+    :target: https://coveralls.io/github/exa-analytics/exatomic
     :alt: Code Coverage
 
 .. |lic| image:: http://img.shields.io/:license-apache-blue.svg?style=flat-square
     :target: http://www.apache.org/licenses/LICENSE-2.0
     :alt: License
-
-Installation
-##################
-.. code-block:: bash
-
-    pip install exa
-    pip install exatomic
-    # Coming soon!
-    # conda install exatomic
-
-
-Getting Started
-##################
-.. code-block:: bash
-
-    exa -u    #  Use the "-u" option on first run
-
-
-Documentation
-###################
-.. code-block:: bash
-
-    cd doc
-    make html    # .\make.bat html # Windows
-
-Legal
-###############
-| Copyright (c) 2015-2016, Exa Analytics Development Team
-| Distributed under the terms of the Apache License 2.0

bld.bat

@@ -0,0 +1,3 @@
+pip install sphinxcontrib-autoanysrc
+"%PYTHON%" setup.py install
+if errorlevel 1 exit 1

build.sh

@@ -0,0 +1,3 @@
+#!/bin/bash
+pip install sphinxcontrib-autoanysrc
+$PYTHON setup.py install

exatomic/__init__.py

@@ -18,7 +18,7 @@
 
 .. _atomic: https://en.wikipedia.org/wiki/Atomic_units
 """
-__exatomic_version__ = (0, 2, 12)
+__exatomic_version__ = (0, 2, 14)
 __version__ = '.'.join((str(v) for v in __exatomic_version__))
 
 

exatomic/_nbextension/app.js

@@ -178,15 +178,28 @@ define([
             });
 
             this.display = {
+                'labels': false,
+                'symbols': false,
                 'cell': false,
                 'axis': false,
                 'spheres': false,
             };
             this.display['folder'] = this.gui.addFolder('display');
+            this.display['labels_checkbox'] = this.display.folder.add(this.display, 'labels');
+            this.display['symbols_checkbox'] = this.display.folder.add(this.display, 'symbols');
             this.display['cell_checkbox'] = this.display.folder.add(this.display, 'cell');
             this.display['axis_checkbox'] = this.display.folder.add(this.display, 'axis');
             this.display['spheres_checkbox'] = this.display.folder.add(this.display, 'spheres');
 
+            /*this.display.labels_checkbox.onFinishChange(function(value) {
+                self.display.labels = value;
+                if (value === false) {
+                    self.app3d.remove(self.label_meshes);
+                } else {
+                    self.render_labels();
+                };
+            });*/
+
             this.display.cell_checkbox.onFinishChange(function(value) {
                 self.display.cell = value;
                 if (value === false) {
@@ -539,6 +552,7 @@ define([
             this.app3d.remove_meshes(this.cell_meshes);
             this.cell_meshes = this.app3d.add_wireframe(vertices);
         };
+
     };
 
     return UniverseApp;

exatomic/algorithms/distance.py

@@ -101,6 +101,46 @@ def periodic_pdist_euc_dxyz_idx(ux, uy, uz, rx, ry, rz, idxs, tol=10**-8):
     return dx, dy, dz, dr, idxi, idxj, px, py, pz
 
 
+def _compute(cx, cy, cz, rx, ry, rz, ox, oy, oz):
+    """
+    """
+    l = [-1, 0, 1]
+    m = len(cx)
+    dx = np.empty((m, ), dtype=np.float64)
+    dy = np.empty((m, ), dtype=np.float64)
+    dz = np.empty((m, ), dtype=np.float64)
+    px = np.empty((27, ), dtype=np.float64)
+    py = np.empty((27, ), dtype=np.float64)
+    pz = np.empty((27, ), dtype=np.float64)
+    pr = np.empty((27, ), dtype=np.float64)
+    h = 0
+    for i in range(m):
+        cxi = cx[i]
+        cyi = cy[i]
+        czi = cz[i]
+        hh = 0
+        for ii in l:
+            for jj in l:
+                for kk in l:
+                    sx = ii*rx
+                    sy = jj*ry
+                    sz = kk*rz
+                    xx = cxi + sx
+                    yy = cyi + sy
+                    zz = czi + sz
+                    pr[hh] = (ox - xx)**2 + (oy - yy)**2 + (oz - zz)**2
+                    px[hh] = sx
+                    py[hh] = sy
+                    pz[hh] = sz
+                    hh += 1
+        hh = np.argmin(pr)
+        dx[h] = px[hh]
+        dy[h] = py[hh]
+        dz[h] = pz[hh]
+        h += 1
+    return dx, dy, dz
+
+
 if config['dynamic']['numba'] == 'true':
     from numba import jit, vectorize
     from exa.math.vector.cartesian import magnitude_xyz
@@ -109,3 +149,4 @@ def periodic_pdist_euc_dxyz_idx(ux, uy, uz, rx, ry, rz, idxs, tol=10**-8):
     minimal_image_counts = jit(nopython=True, cache=True, nogil=True)(minimal_image_counts)
     minimal_image = vectorize(types3, nopython=True)(minimal_image)
     periodic_pdist_euc_dxyz_idx = jit(nopython=True, cache=True, nogil=True)(periodic_pdist_euc_dxyz_idx)
+    _compute = jit(nopython=True, cache=True, nogil=True)(_compute)

exatomic/algorithms/neighbors.py

@@ -14,7 +14,8 @@
 and :func:`~exatomic.molecule.Molecule.classify`.
 """
 import numpy as np
-from exatomic.container import Universe
+import pandas as pd
+from exatomic.algorithms.distance import _compute
 
 
 def nearest_molecules(universe, n, sources, restrictions=None, how='atom',
@@ -46,13 +47,17 @@ def nearest_molecules(universe, n, sources, restrictions=None, how='atom',
     Returns:
         unis (dict): Dictionary of number of neighbors keys, universe values
     """
-    source_atoms, other_atoms, source_molecules, other_molecules, n = _slice_atoms_molecules(universe, sources, restrictions, how)
+    source_atoms, other_atoms, source_molecules, other_molecules, n = _slice_atoms_molecules(universe, sources, restrictions, n)
     ordered_molecules, ordered_twos = _compute_neighbors_by_atom(universe, source_atoms, other_atoms, source_molecules)
-    unis = {nn: _build_universe(universe, ordered_molecules, ordered_twos, nn) for nn in n}
-
-
-
-
+    unis = {}
+    if free_boundary == True:
+        for nn in n:
+            unis[nn] = _build_free_universe(universe, ordered_molecules,
+                                            ordered_twos, nn, source_atoms,
+                                            source_molecules)
+    else:
+        raise NotImplementedError()
+    return unis
 
 
 def _slice_atoms_molecules(universe, sources, restrictions, n):
@@ -66,7 +71,7 @@ def _slice_atoms_molecules(universe, sources, restrictions, n):
         sources = [sources]
     if not isinstance(restrictions, list) and restrictions is not None:
         restrictions = [restrictions]
-    if not isinstance(n, list):
+    if isinstance(n, (int, np.int32, np.int64)):
         n = [n]
     symbols = universe.atom['symbol'].unique()
     classification = universe.molecule['classification'].unique()
@@ -135,12 +140,68 @@ def _compute_neighbors_by_com(universe, source_molecules, other_molecules):
     raise NotImplementedError()
 
 
+def _build_free_universe(universe, ordered_molecules, ordered_twos, n,
+                         source_atoms, source_molecules):
+    """
+    """
+    molecule = np.concatenate([mcules[:n] for mcules in ordered_molecules])
+    molecule = np.concatenate((molecule, source_molecules.index.tolist()))
+    molecule = universe.molecule[universe.molecule.index.isin(molecule)].copy()
+    atom = universe.atom[universe.atom['molecule'].isin(molecule.index)].copy()
+    atom_two = universe.atom_two[(universe.atom_two['atom0'].isin(atom.index) &
+                                  universe.atom_two['atom1'].isin(atom.index))].copy()
+    frame = universe.frame[universe.frame.index.isin(atom['frame'])].copy()
+    frame['periodic'] = False
+    uni = universe.__class__(atom=atom, molecule=molecule, frame=frame, atom_two=atom_two)
+    if universe.frame.is_periodic():
+        uni.atom.update(universe.visual_atom)
+        if 'cx' not in uni.molecule.columns:
+            uni.compute_molecule_com()
+        uni.atom._revert_categories()
+        mapper = uni.atom.drop_duplicates('molecule').set_index('molecule')['frame']
+        uni.atom._set_categories()
+        uni.molecule['frame'] = uni.molecule.index.map(lambda x: mapper[x])
+        sources = source_atoms.groupby('frame')
+        groups = uni.molecule.groupby('frame')
+        n = groups.ngroups
+        dx = np.empty((n, ), dtype=np.ndarray)
+        dy = np.empty((n, ), dtype=np.ndarray)
+        dz = np.empty((n, ), dtype=np.ndarray)
+        index = np.empty((n, ), dtype=np.ndarray)
+        for i, (frame, group) in enumerate(groups):
+            cx = group['cx'].values
+            cy = group['cy'].values
+            cz = group['cz'].values
+            ccx, ccy, ccz = sources.get_group(frame)[['x', 'y', 'z']].mean().values
+    #        ccx, ccy, ccz = mcules.ix[mcules['classification'] == 'solute', ['cx', 'cy', 'cz']].values[0]
+            rx, ry, rz = uni.frame.ix[frame, ['rx', 'ry', 'rz']].values
+            dxf, dyf, dzf = _compute(cx, cy, cz, rx, ry, rz, ccx, ccy, ccz)
+            dx[i] = dxf
+            dy[i] = dyf
+            dz[i] = dzf
+            index[i] = group.index.values
+        del uni.molecule['frame']
+        dx = np.concatenate(dx)
+        dy = np.concatenate(dy)
+        dz = np.concatenate(dz)
+        index = np.concatenate(index)
+        df = pd.DataFrame.from_dict({'x': dx, 'y': dy, 'z': dz, 'molecule': index})
+        df.set_index('molecule', inplace=True)
+        for molecule in df.index:
+            dx, dy, dz = df.ix[molecule].values
+            uni.atom.ix[uni.atom['molecule'] == molecule, 'x'] += dx
+            uni.atom.ix[uni.atom['molecule'] == molecule, 'y'] += dy
+            uni.atom.ix[uni.atom['molecule'] == molecule, 'z'] += dz
+    return uni
+
+
 def _build_universe(universe, ordered_molecules, ordered_twos, n):
     """
     """
+    raise NotImplementedError()
     # TODO CONVERT TO A GENERIC AND COMPLETE SLICER
-    molecules = np.concatenate([m[:n] for m in ordered_molecules.values])
-    twos = np.concatenate([t[:n] for t in ordered_twos.values])
+    molecules = np.concatenate([m[:n] for m in ordered_molecules])
+    twos = np.concatenate([t[:n] for t in ordered_twos])
     atom = universe.atom[universe.atom['molecule'].isin(molecules)].copy().sort_index()
     two = universe.atom_two[universe.atom_two['atom0'].isin(atom.index) &
                             universe.atom_two['atom1'].isin(atom.index)].copy().sort_index()