def addCoordset(self, coords, allcoordsets=True):
"""Add coordinate set(s) as conformation(s).
:class:`~prody.atomic.Atomic` instances are accepted as *coords*
argument. If *allcoordsets* is ``True``, all coordinate sets from
the :class:`~prody.atomic.Atomic` instance will be appended to the
ensemble. Otherwise, only the active coordinate set will be appended.
"""
assert isinstance(allcoordsets, bool), 'allcoordsets must be boolean'
if not isinstance(coords, np.ndarray):
if isinstance(coords, (Atomic, Ensemble)):
atoms = coords
if allcoordsets:
coords = atoms.getCoordsets()
else:
coords = atoms.getCoords()
if coords is None:
raise ValueError('{0:s} must contain coordinate data'
.format(atoms))
else:
raise TypeError('coords must be a Numpy array or '
'ProDy Atomic or Ensemble instance')
coords = checkCoords(coords, arg='coords', cset=True,
n_atoms=self._n_atoms, reshape=True)
if self._n_atoms == 0:
self._n_atoms = coords.shape[-2]
n_confs = coords.shape[0]
if self._confs is None:
self._confs = coords
else:
self._confs = np.concatenate((self._confs, coords), axis=0)
self._n_csets += n_confs
def addCoordset(self, coords, label=None):
"""Add a coordinate set. *coords* argument may be an object instance
with ``getCoordsets`` method."""
if not isinstance(coords, np.ndarray):
coords = coords.getCoordsets()
if self._coords is None:
self.setCoords(coords)
return
coords = checkCoords(coords, 'coords', cset=True,
n_atoms=self._n_atoms, reshape=True)
diff = coords.shape[0]
self._coords = np.concatenate((self._coords, coords), axis=0)
self._n_csets = self._coords.shape[0]
timestamps = self._timestamps
self._timestamps = np.zeros(self._n_csets)
self._timestamps[:len(timestamps)] = timestamps
self._timestamps[len(timestamps):] = time()
self._kdtrees.extend([None] * diff)
if isinstance(label, (str, NoneType)):
self._cslabels += [label] * diff
elif isinstance(label, (list, tuple)):
if len(label) == diff:
if all([isinstance(lbl, str) for lbl in label]):
self._cslabels += label
else:
LOGGER.warning('all items of `label` must be strings')
else:
LOGGER.warning('`label` list must have same length as the '
'`coords` array')
else:
LOGGER.warning('`label` must be a string or list of strings')
def setCoords(self, coords):
"""Set reference coordinates."""
if not isinstance(coords, np.ndarray):
try:
coords = coords.getCoords()
except AttributeError:
raise TypeError('coords must be a Numpy array or must have '
'getCoordinates attribute')
self._coords = checkCoords(coords, arg='coords',
n_atoms=self._n_atoms, cset=False)
def addCoordset(self, coords, weights=None, allcoordsets=True):
"""Add coordinate set(s) as conformation(s).
:class:`~prody.atomic.Atomic` instances are accepted as *coords*
argument. If *allcoordsets* is ``True``, all coordinate sets from
the :class:`~prody.atomic.Atomic` instance will be appended to the
ensemble. Otherwise, only the active coordinate set will be appended.
*weights* is an optional argument. If provided, its length must
match number of atoms. Weights of missing (not resolved) atoms
must be equal to ``0`` and weights of those that are resolved
can be anything greater than ``0``. If not provided, weights of
atoms in this coordinate set will be set equal to ``1``."""
assert isinstance(allcoordsets, bool), 'allcoordsets must be boolean'
if weights is not None:
assert isinstance(weights, np.ndarray), 'weights must be ndarray'
ag = None
if isinstance(coords, Atomic):
atoms = coords
if isinstance(coords, AtomGroup):
ag = atoms
else:
ag = atoms.getAtomGroup()
if allcoordsets:
coords = atoms.getCoordsets()
else:
coords = atoms.getCoords()
title = ag.getTitle()
elif isinstance(coords, np.ndarray):
title = 'Unknown'
else:
title = str(coords)
try:
if allcoordsets:
coords = coords.getCoordsets()
else:
coords = coords.getCoords()
except AttributeError:
raise TypeError('coords must be a Numpy array or must have '
'getCoordinates attribute')
coords = checkCoords(coords, 'coords', cset=True,
n_atoms=self._n_atoms, reshape=True)
n_csets, n_atoms, _ = coords.shape
if self._n_atoms == 0:
self._n_atoms = n_atoms
if weights is None:
weights = np.ones((n_csets, n_atoms, 1), dtype=float)
else:
weights = checkWeights(weights, n_atoms, n_csets)
while ' ' in title:
title = title.replace(' ', ' ')
title = title.replace(' ', '_')
if n_csets > 1:
self._labels += ['{0:s}_{1:d}'
.format(title, i+1) for i in range(n_csets)]
else:
if ag is not None and ag.numCoordsets() > 1:
self._labels.append('{0:s}_{1:d}'.format(title,
atoms.getACSIndex()))
else:
self._labels.append(title)
if self._confs is None and self._weights is None:
self._confs = coords
self._weights = weights
self._n_csets = n_csets
elif self._confs is not None and self._weights is not None:
self._confs = np.concatenate((self._confs, coords), axis=0)
self._weights = np.concatenate((self._weights, weights), axis=0)
self._n_csets += n_csets
else:
raise RuntimeError('_confs and _weights must be set or None at '
'the same time')
def buildHessian(self, coords, cutoff=15.0, gamma=1.0, **kwargs):
"""Build Hessian matrix for given coordinate set.
:arg coords: a coordinate set or anything with getCoordinates method
:type coords: :class:`~numpy.ndarray` or :class:`~prody.atomic.Atomic`
:arg cutoff: cutoff distance (Å) for pairwise interactions,
default is 15.0 Å, minimum is 4.0 Å
:type cutoff: float
:arg gamma: spring constant, default is 1.0
:type gamma: float, :class:`Gamma`
:arg sparse: slect to use sparse matrices. Default is ``False``. If
Scipy is not found, :class:`ImportError` is raised.
:type sparse: bool
Instances of :class:`Gamma` classes and custom functions are
accepted as *gamma* argument.
When Scipy is available, user can select to use sparse matrices for
efficient usage of memory at the cost of computation speed."""
slow = kwargs.get("slow", False)
try:
from KDTree import KDTree
except ImportError:
KDTree = False
if not slow and not KDTree:
LOGGER.info("Using a slower method for building the Hessian " "matrix.")
if not isinstance(coords, np.ndarray):
try:
coords = coords.getCoords()
except AttributeError:
raise TypeError("coords must be a Numpy array or must have " "getCoordinates attribute")
coords = checkCoords(coords, "coords")
cutoff, g, gamma = checkENMParameters(cutoff, gamma)
self._reset()
self._cutoff = cutoff
self._gamma = g
n_atoms = coords.shape[0]
dof = n_atoms * 3
start = time.time()
if kwargs.get("sparse", False):
try:
from scipy import sparse as scipy_sparse
except ImportError:
raise ImportError("failed to import scipy.sparse, which is " "required for sparse matrix calculations")
kirchhoff = scipy_sparse.lil_matrix((n_atoms, n_atoms))
hessian = scipy_sparse.lil_matrix((dof, dof))
else:
kirchhoff = np.zeros((n_atoms, n_atoms), "d")
hessian = np.zeros((dof, dof), float)
if not slow and KDTree:
kdtree = getKDTree(coords)
kdtree.all_search(cutoff)
for i, j in kdtree.all_get_indices():
i2j = coords[j] - coords[i]
dist2 = np.dot(i2j, i2j)
g = gamma(dist2, i, j)
super_element = np.outer(i2j, i2j) * (-g / dist2)
res_i3 = i * 3
res_i33 = res_i3 + 3
res_j3 = j * 3
res_j33 = res_j3 + 3
hessian[res_i3:res_i33, res_j3:res_j33] = super_element
hessian[res_j3:res_j33, res_i3:res_i33] = super_element
hessian[res_i3:res_i33, res_i3:res_i33] = hessian[res_i3:res_i33, res_i3:res_i33] - super_element
hessian[res_j3:res_j33, res_j3:res_j33] = hessian[res_j3:res_j33, res_j3:res_j33] - super_element
kirchhoff[i, j] = -g
kirchhoff[j, i] = -g
kirchhoff[i, i] = kirchhoff[i, i] - g
kirchhoff[j, j] = kirchhoff[j, j] - g
else:
cutoff2 = cutoff * cutoff
for i in range(n_atoms):
res_i3 = i * 3
res_i33 = res_i3 + 3
xyz_i = coords[i, :]
for j in range(i + 1, n_atoms):
i2j = coords[j, :] - xyz_i
dist2 = np.dot(i2j, i2j)
if dist2 > cutoff2:
continue
g = gamma(dist2, i, j)
res_j3 = j * 3
res_j33 = res_j3 + 3
super_element = np.outer(i2j, i2j) * (-g / dist2)
hessian[res_i3:res_i33, res_j3:res_j33] = super_element
hessian[res_j3:res_j33, res_i3:res_i33] = super_element
hessian[res_i3:res_i33, res_i3:res_i33] = hessian[res_i3:res_i33, res_i3:res_i33] - super_element
hessian[res_j3:res_j33, res_j3:res_j33] = hessian[res_j3:res_j33, res_j3:res_j33] - super_element
kirchhoff[i, j] = -g
kirchhoff[j, i] = -g
kirchhoff[i, i] = kirchhoff[i, i] - g
kirchhoff[j, j] = kirchhoff[j, j] - g
LOGGER.info("Hessian was built in {0:.2f}s.".format(time.time() - start))
self._kirchhoff = kirchhoff
self._hessian = hessian
self._n_atoms = n_atoms
self._dof = dof
def setCoords(self, coords, label=None):
"""Set coordinates. *coords* may be a :class:`numpy.ndarray` instance
or an object instance with ``getCoordsets`` method. If the shape of
the coordinates array is (n_csets,n_atoms,3), the given array will
replace all coordinate sets. To avoid it, :meth:`addCoordset` may be
used. If the shape of the *coords* array is (n_atoms,3) or
(1,n_atoms,3), the coordinate set will replace the coordinates of the
currently active set. *label* argument may be used to label coordinate
sets. *label* may be a string or a list of strings length equal to
the number of coordinate sets."""
if not isinstance(coords, np.ndarray):
coords = coords.getCoordsets()
coords = checkCoords(coords, 'coords',
cset=True, n_atoms=self._n_atoms,
reshape=True)
if self._n_atoms == 0:
self._n_atoms = coords.shape[-2]
acsi = None
if self._coords is None:
self._coords = coords
self._n_csets = coords.shape[0]
self._acsi = 0
self._setTimeStamp()
if isinstance(label, (NoneType, str)):
self._cslabels = [label] * self._n_csets
elif isinstance(label, (list, tuple)):
if len(label) == self._n_csets:
self._cslabels = label
else:
self._cslabels = [None] * self._n_csets
LOGGER.warning('Length of `label` does not match number '
'of coordinate sets.')
else:
if coords.shape[0] == 1:
acsi = self._acsi
self._coords[acsi] = coords[0]
self._setTimeStamp(acsi)
if isinstance(label, str):
self._cslabels[self._acsi] = label
else:
self._coords = coords
self._n_csets = coords.shape[0]
self._acsi = min(self._n_csets - 1, self._acsi)
self._setTimeStamp()
if acsi is None:
if isinstance(label, (str, NoneType)):
self._cslabels = [label] * self._n_csets
elif isinstance(label, (list, tuple)):
if len(label) == self._n_csets:
if all([isinstance(lbl, str) for lbl in label]):
self._cslabels += label
else:
LOGGER.warning('all items of label must be strings')
else:
LOGGER.warning('label must have same length as the '
'coords array')
else:
LOGGER.warning('label must be a string or list of strings')
elif label is not None:
if isinstance(label, str):
self._cslabels[acsi] = label
elif isinstance(label, (list, tuple)):
if len(label) == 1:
if isinstance(label[0], str):
self._cslabels[acsi] = label
else:
LOGGER.warning('all items of label must be strings')
else:
LOGGER.warning('length of label must be one')
else:
LOGGER.warning('label must be a string or list of strings')
def buildKirchhoff(self, coords, cutoff=10., gamma=1., **kwargs):
"""Build Kirchhoff matrix for given coordinate set.
:arg coords: a coordinate set or anything with getCoordinates method
:type coords: :class:`~numpy.ndarray` or :class:`~prody.atomic.Atomic`
:arg cutoff: cutoff distance (Å) for pairwise interactions
default is 10.0 Å, , minimum is 4.0 Å
:type cutoff: float
:arg gamma: spring constant, default is 1.0
:type gamma: float
:arg sparse: Elect to use sparse matrices. Default is ``False``. If
Scipy is not found, :class:`ImportError` is raised.
:type sparse: bool
Instances of :class:`Gamma` classes and custom functions are
accepted as *gamma* argument.
When Scipy is available, user can select to use sparse matrices for
efficient usage of memory at the cost of computation speed."""
slow = kwargs.get('slow', False)
try:
from KDTree import KDTree
except ImportError:
KDTree = False
if not slow and not KDTree:
LOGGER.info('Using a slower method for building the Kirchhoff '
'matrix.')
if not isinstance(coords, np.ndarray):
try:
coords = coords.getCoords()
except AttributeError:
raise TypeError('coords must be a Numpy array or must have '
'getCoordinates attribute')
coords = checkCoords(coords, 'coords')
cutoff, g, gamma = checkENMParameters(cutoff, gamma)
self._reset()
self._cutoff = cutoff
self._gamma = g
n_atoms = coords.shape[0]
start = time.time()
if kwargs.get('sparse', False):
try:
from scipy import sparse as scipy_sparse
except ImportError:
raise ImportError('failed to import scipy.sparse, which is '
'required for sparse matrix calculations')
kirchhoff = scipy_sparse.lil_matrix((n_atoms, n_atoms))
else:
kirchhoff = np.zeros((n_atoms, n_atoms), 'd')
if not slow and KDTree:
kdtree = getKDTree(coords)
kdtree.all_search(cutoff)
radii = kdtree.all_get_radii()
r = 0
for i, j in kdtree.all_get_indices():
g = gamma(radii[r]**2, i, j)
kirchhoff[i, j] = -g
kirchhoff[j, i] = -g
kirchhoff[i, i] = kirchhoff[i, i] + g
kirchhoff[j, j] = kirchhoff[j, j] + g
r += 1
else:
cutoff2 = cutoff * cutoff
for i in range(n_atoms):
xyz_i = coords[i, :]
for j in range(i+1, n_atoms):
i2j = coords[j, :] - xyz_i
dist2 = np.dot(i2j, i2j)
if dist2 > cutoff2:
continue
g = gamma(dist2, i, j)
kirchhoff[i, j] = -g
kirchhoff[j, i] = -g
kirchhoff[i, i] = kirchhoff[i, i] + g
kirchhoff[j, j] = kirchhoff[j, j] + g
LOGGER.debug('Kirchhoff was built in {0:.2f}s.'
.format(time.time()-start))
self._kirchhoff = kirchhoff
self._n_atoms = n_atoms
self._dof = n_atoms
def write(self, coords, unitcell=None, **kwargs):
"""Write *coords* to the file. Number of atoms will be determined
based on the size of the first coordinate set. If *unitcell* is
provided for the first coordinate set, it will be expected for the
following coordinate sets as well.
The following keywords are used when writing the first coordinate set
for files open at 'w' mode:
:arg timestep: timestep used for integration, default is 1
:arg firsttimestep: number of the first timestep, default is 0
:arg framefreq: number of timesteps between frames, default is 1"""
if self._closed:
raise ValueError('I/O operation on closed file')
if self._mode == 'r':
raise IOError('File not open for writing')
coords = checkCoords(coords, 'coords', True, dtype=np.float32)
if coords.ndim == 2:
n_atoms = coords.shape[0]
coords = [coords]
else:
n_atoms = coords.shape[1]
if self._n_atoms == 0:
self._n_atoms = n_atoms
else:
if self._n_atoms != n_atoms:
raise ValueError('coords to not have correct number '
'of atoms')
dcd = self._file
pack_i_4N = pack('i', self._n_atoms * 4)
if self._n_csets == 0:
if unitcell is None:
self._unitcell = False
else:
self._unitcell = True
timestep = float(kwargs.get('timestep', 1.0))
first_ts = int(kwargs.get('firsttimestep', 0))
framefreq = int(kwargs.get('framefreq', 1))
n_fixed = 0
pack_i_0 = pack('i', 0)
pack_ix4_0x4 = pack('i'*4, 0, 0, 0, 0)
pack_i_1 = pack('i', 1)
pack_i_2 = pack('i', 2)
pack_i_4 = pack('i', 4)
pack_i_84 = pack('i', 84)
pack_i_164 = pack('i', 164)
dcd.write(pack_i_84)
dcd.write('CORD')
dcd.write(pack_i_0) # 0 Number of frames in file, none written yet
dcd.write(pack('i', first_ts)) # 1 Starting timestep
dcd.write(pack('i', framefreq)) # 2 Timesteps between frames
dcd.write(pack_i_0) # 3 Number of timesteps in simulation
dcd.write(pack_i_0) # 4 NAMD writes NSTEP or ISTART - NSAVC here?
dcd.write(pack_ix4_0x4) # 5, 6, 7, 8
dcd.write(pack('f', timestep)) # 9 timestep
dcd.write(pack('i', int(self._unitcell))) # 10 with unitcell
dcd.write(pack_ix4_0x4) # 11, 12, 13, 14
dcd.write(pack_ix4_0x4) # 15, 16, 17, 18
dcd.write(pack('i', 24)) # 19 Pretend to be CHARMM version 24
dcd.write(pack_i_84)
dcd.write(pack_i_164)
dcd.write(pack_i_2)
dcd.write('{0:80s}'.format('Created by ProDy'))
dcd.write('{0:80s}'.format('REMARKS Created ' +
now().strftime('%d %B, %Y at %H:%M')))
dcd.write(pack_i_164)
dcd.write(pack_i_4)
dcd.write(pack('i', n_atoms))
dcd.write(pack_i_4)
self._first_byte = dcd.tell()
if self._unitcell:
if unitcell is None:
raise TypeError('unitcell data is expected')
else:
uc = unitcell
uc[3:] = np.sin((PISQUARE/90) * (90-uc[3:]))
uc = uc[[0,3,1,4,5,2]]
pack_i_48 = pack('i', 48)
dcd.seek(0, 2)
for xyz in coords:
if self._unitcell:
dcd.write(pack_i_48)
uc.tofile(dcd)
dcd.write(pack_i_48)
xyz = xyz.T
dcd.write(pack_i_4N)
xyz[0].tofile(dcd)
dcd.write(pack_i_4N)
dcd.write(pack_i_4N)
xyz[1].tofile(dcd)
dcd.write(pack_i_4N)
dcd.write(pack_i_4N)
xyz[2].tofile(dcd)
dcd.write(pack_i_4N)
self._n_csets += 1
dcd.seek(8, 0)
dcd.write(pack('i', self._n_csets))
dcd.seek(0, 2)
self._nfi = self._n_csets
