def display(self, array, site=None):
if site is not None:
self.im.putpixel(site, self.grayscale(array[site]))
else:
scaleddata = self.simpgrayscale(array.transpose().flatten())
self.im.putdata(scaleddata)
self.displayIm = self.im.resize(self.size)
self.tkimage.paste(self.displayIm)
self.canvas.update()
def _crossCorrelations(queue, n_atoms, array, variances, indices):
"""Calculate covariance-matrix for a subset of modes."""
n_modes = len(indices)
arvar = (array[:, indices] * variances[indices]).T.reshape(
(n_modes, n_atoms, 3))
array = array[:, indices].T.reshape((n_modes, n_atoms, 3))
covariance = np.tensordot(array.transpose(2, 0, 1),
arvar.transpose(0, 2, 1),
axes=([0, 1], [1, 0]))
queue.put(covariance)
def _crossCorrelations(queue, n_atoms, array, variances, indices):
"""Calculate covariance-matrix for a subset of modes."""
n_modes = len(indices)
arvar = (array[:, indices] * variances[indices]).T.reshape((n_modes,
n_atoms, 3))
array = array[:, indices].T.reshape((n_modes, n_atoms, 3))
covariance = np.tensordot(array.transpose(2, 0, 1),
arvar.transpose(0, 2, 1),
axes=([0, 1], [1, 0]))
queue.put(covariance)
file_size = f.tell()
progress.text_file_size(file_size)
progress.close_on_cancel(f)
f.seek(byte_offset)
try:
read_float_lines(f, array, line_format, progress)
except SyntaxError, msg:
f.close()
raise
f.close()
if transpose:
array = array.transpose()
if progress:
progress.done()
return array
# -----------------------------------------------------------------------------
#
def read_float_lines(f, array, line_format, progress=None):
a_1d = array.ravel()
count = len(a_1d)
c = 0
def calcCrossCorr(modes, n_cpu=1, norm=True):
"""Returns cross-correlations matrix. For a 3-d model, cross-correlations
matrix is an NxN matrix, where N is the number of atoms. Each element of
this matrix is the trace of the submatrix corresponding to a pair of atoms.
Covariance matrix may be calculated using all modes or a subset of modes
of an NMA instance. For large systems, calculation of cross-correlations
matrix may be time consuming. Optionally, multiple processors may be
employed to perform calculations by passing ``n_cpu=2`` or more."""
if not isinstance(n_cpu, int):
raise TypeError('n_cpu must be an integer')
elif n_cpu < 1:
raise ValueError('n_cpu must be equal to or greater than 1')
if not isinstance(modes, (Mode, NMA, ModeSet)):
raise TypeError('modes must be a Mode, NMA, or ModeSet instance, '
'not {0}'.format(type(modes)))
if modes.is3d():
model = modes
if isinstance(modes, (Mode, ModeSet)):
model = modes._model
if isinstance(modes, (Mode)):
indices = [modes.getIndex()]
n_modes = 1
else:
indices = modes.getIndices()
n_modes = len(modes)
else:
n_modes = len(modes)
indices = np.arange(n_modes)
array = model._getArray()
n_atoms = model._n_atoms
variances = model._vars
if n_cpu == 1:
s = (n_modes, n_atoms, 3)
arvar = (array[:, indices] * variances[indices]).T.reshape(s)
array = array[:, indices].T.reshape(s)
covariance = np.tensordot(array.transpose(2, 0, 1),
arvar.transpose(0, 2, 1),
axes=([0, 1], [1, 0]))
else:
import multiprocessing
n_cpu = min(multiprocessing.cpu_count(), n_cpu)
queue = multiprocessing.Queue()
size = n_modes / n_cpu
for i in range(n_cpu):
if n_cpu - i == 1:
indices = modes.indices[i * size:]
else:
indices = modes.indices[i * size:(i + 1) * size]
process = multiprocessing.Process(target=_crossCorrelations,
args=(queue, n_atoms, array,
variances, indices))
process.start()
while queue.qsize() < n_cpu:
time.sleep(0.05)
covariance = queue.get()
while queue.qsize() > 0:
covariance += queue.get()
else:
covariance = calcCovariance(modes)
if norm:
diag = np.power(covariance.diagonal(), 0.5)
covariance /= np.outer(diag, diag)
return covariance
def calcCrossCorr(modes, n_cpu=1, norm=True):
"""Returns cross-correlations matrix. For a 3-d model, cross-correlations
matrix is an NxN matrix, where N is the number of atoms. Each element of
this matrix is the trace of the submatrix corresponding to a pair of atoms.
Covariance matrix may be calculated using all modes or a subset of modes
of an NMA instance. For large systems, calculation of cross-correlations
matrix may be time consuming. Optionally, multiple processors may be
employed to perform calculations by passing ``n_cpu=2`` or more."""
if not isinstance(n_cpu, int):
raise TypeError('n_cpu must be an integer')
elif n_cpu < 1:
raise ValueError('n_cpu must be equal to or greater than 1')
if not isinstance(modes, (Mode, NMA, ModeSet)):
if isinstance(modes, list):
try:
is3d = modes[0].is3d()
except:
raise TypeError('modes must be a list of Mode or Vector instances, '
'not {0}'.format(type(modes)))
else:
raise TypeError('modes must be a Mode, NMA, or ModeSet instance, '
'not {0}'.format(type(modes)))
else:
is3d = modes.is3d()
if is3d:
model = modes
if isinstance(modes, (Mode, ModeSet)):
model = modes._model
if isinstance(modes, (Mode)):
indices = [modes.getIndex()]
n_modes = 1
else:
indices = modes.getIndices()
n_modes = len(modes)
else:
n_modes = len(modes)
indices = np.arange(n_modes)
array = model._getArray()
n_atoms = model._n_atoms
variances = model._vars
if n_cpu == 1:
s = (n_modes, n_atoms, 3)
arvar = (array[:, indices]*variances[indices]).T.reshape(s)
array = array[:, indices].T.reshape(s)
covariance = np.tensordot(array.transpose(2, 0, 1),
arvar.transpose(0, 2, 1),
axes=([0, 1], [1, 0]))
else:
import multiprocessing
n_cpu = min(multiprocessing.cpu_count(), n_cpu)
queue = multiprocessing.Queue()
size = n_modes / n_cpu
for i in range(n_cpu):
if n_cpu - i == 1:
indices = modes.indices[i*size:]
else:
indices = modes.indices[i*size:(i+1)*size]
process = multiprocessing.Process(
target=_crossCorrelations,
args=(queue, n_atoms, array, variances, indices))
process.start()
while queue.qsize() < n_cpu:
time.sleep(0.05)
covariance = queue.get()
while queue.qsize() > 0:
covariance += queue.get()
else:
covariance = calcCovariance(modes)
if norm:
diag = np.power(covariance.diagonal(), 0.5)
D = np.outer(diag, diag)
covariance = div0(covariance, D)
return covariance
file_size = f.tell()
progress.text_file_size(file_size)
progress.close_on_cancel(f)
f.seek(byte_offset)
try:
read_float_lines(f, array, line_format, progress)
except SyntaxError, msg:
f.close()
raise
f.close()
if transpose:
array = array.transpose()
if progress:
progress.done()
return array
# -----------------------------------------------------------------------------
#
def read_float_lines(f, array, line_format, progress = None):
a_1d = array.ravel()
count = len(a_1d)
c = 0
while c < count:
