def showPairDeformationDist(model, coords, ind1, ind2, *args, **kwargs):
"""Show distribution of deformations in distance contributed by each mode
for selected pair of residues *ind1* *ind2*
using :func:`~matplotlib.pyplot.plot`. """
import matplotlib
import matplotlib.pyplot as plt
if not isinstance(model, NMA):
raise TypeError('model must be a NMA instance, '
'not {0}'.format(type(model)))
elif not model.is3d():
raise TypeError('model must be a 3-dimensional NMA instance')
elif len(model) == 0:
raise ValueError('model must have normal modes calculated')
elif model.getStiffness() is None:
raise ValueError('model must have stiffness matrix calculated')
d_pair = calcPairDeformationDist(model, coords, ind1, ind2)
with plt.style.context('fivethirtyeight'):
matplotlib.rcParams['font.size'] = '16'
fig = plt.figure(num=None, figsize=(12,8), dpi=100, facecolor='w')
#plt.title(str(model))
plt.plot(d_pair[0], d_pair[1], 'k-', linewidth=1.5, *args, **kwargs)
plt.xlabel('mode (k)', fontsize = '18')
plt.ylabel('d$^k$' '($\AA$)', fontsize = '18')
if SETTINGS['auto_show']:
showFigure()
return plt.show
def showNormDistFunct(model, coords, *args, **kwargs):
"""Show normalized distance fluctuation matrix using
:func:`~matplotlib.pyplot.imshow`. By default, *origin=lower*
keyword arguments are passed to this function,
but user can overwrite these parameters."""
import math
import matplotlib
import matplotlib.pyplot as plt
normdistfunct = model.getNormDistFluct(coords)
if not 'origin' in kwargs:
kwargs['origin'] = 'lower'
matplotlib.rcParams['font.size'] = '14'
fig = plt.figure(num=None, figsize=(10,8), dpi=100, facecolor='w')
show = plt.imshow(normdistfunct, *args, **kwargs), plt.colorbar()
plt.clim(math.floor(np.min(normdistfunct[np.nonzero(normdistfunct)])), \
round(np.amax(normdistfunct),1))
plt.title('Normalized Distance Fluctution Matrix')
plt.xlabel('Indices', fontsize='16')
plt.ylabel('Indices', fontsize='16')
if SETTINGS['auto_show']:
showFigure()
return show
def showLinkage(V, **kwargs):
"""Shows the dendrogram of hierarchical clustering on *V*. See :func:`scipy.cluster.hierarchy.dendrogram` for details.
:arg V: row-normalized eigenvectors for the purpose of clustering.
:type V: :class:`numpy.ndarray`
"""
from .functions import _getEigvecs
V = _getEigvecs(V, row_norm=True)
try:
from scipy.cluster.hierarchy import linkage, fcluster, dendrogram
except ImportError:
raise ImportError('Use of this function (showLinkage) requires the '
'installation of scipy.')
method = kwargs.pop('method', 'single')
metric = kwargs.pop('metric', 'euclidean')
Z = linkage(V, method=method, metric=metric)
no_labels = kwargs.pop('no_labels', True)
dendrogram(Z, no_labels=no_labels, **kwargs)
if SETTINGS['auto_show']:
showFigure()
return Z
def showScaledSqFlucts(modes, *args, **kwargs):
"""Show scaled square fluctuations using :func:`~matplotlib.pyplot.plot`.
Modes or mode sets given as additional arguments will be scaled to have
the same mean squared fluctuations as *modes*."""
import matplotlib.pyplot as plt
sqf = calcSqFlucts(modes)
mean = sqf.mean()
args = list(args)
modesarg = []
i = 0
while i < len(args):
if isinstance(args[i], (VectorBase, ModeSet, NMA)):
modesarg.append(args.pop(i))
else:
i += 1
show = [plt.plot(sqf, *args, label=str(modes), **kwargs)]
plt.xlabel('Indices')
plt.ylabel('Square fluctuations')
for modes in modesarg:
sqf = calcSqFlucts(modes)
scalar = mean / sqf.mean()
show.append(plt.plot(sqf * scalar, *args,
label='{0} (x{1:.2f})'.format(str(modes), scalar),
**kwargs))
if SETTINGS['auto_show']:
showFigure()
return show
def showNormedSqFlucts(modes, *args, **kwargs):
"""Show normalized square fluctuations via :func:`~matplotlib.pyplot.plot`.
"""
import matplotlib.pyplot as plt
sqf = calcSqFlucts(modes)
args = list(args)
modesarg = []
i = 0
while i < len(args):
if isinstance(args[i], (VectorBase, ModeSet, NMA)):
modesarg.append(args.pop(i))
else:
i += 1
show = [
plt.plot(sqf / (sqf**2).sum()**0.5,
*args,
label='{0}'.format(str(modes)),
**kwargs)
]
plt.xlabel('Indices')
plt.ylabel('Square fluctuations')
for modes in modesarg:
sqf = calcSqFlucts(modes)
show.append(
plt.plot(sqf / (sqf**2).sum()**0.5,
*args,
label='{0}'.format(str(modes)),
**kwargs))
if SETTINGS['auto_show']:
showFigure()
return show
def showOverlapTable(modes_x, modes_y, **kwargs):
"""Show overlap table using :func:`~matplotlib.pyplot.pcolor`. *modes_x*
and *modes_y* are sets of normal modes, and correspond to x and y axes of
the plot. Note that mode indices are incremented by 1. List of modes
is assumed to contain a set of contiguous modes from the same model.
Default arguments for :func:`~matplotlib.pyplot.pcolor`:
* ``cmap=plt.cm.jet``
* ``norm=matplotlib.colors.Normalize(0, 1)``"""
import matplotlib.pyplot as plt
overlap = abs(calcOverlap(modes_y, modes_x))
if overlap.ndim == 0:
overlap = np.array([[overlap]])
elif overlap.ndim == 1:
overlap = overlap.reshape((modes_y.numModes(), modes_x.numModes()))
cmap = kwargs.pop('cmap', plt.cm.jet)
norm = kwargs.pop('norm', matplotlib.colors.Normalize(0, 1))
show = (plt.pcolor(overlap, cmap=cmap, norm=norm,
**kwargs), plt.colorbar())
x_range = np.arange(1, modes_x.numModes() + 1)
plt.xticks(x_range - 0.5, x_range)
plt.xlabel(str(modes_x))
y_range = np.arange(1, modes_y.numModes() + 1)
plt.yticks(y_range - 0.5, y_range)
plt.ylabel(str(modes_y))
plt.axis([0, modes_x.numModes(), 0, modes_y.numModes()])
if SETTINGS['auto_show']:
showFigure()
return show
def showNormDistFunct(model, coords, *args, **kwargs):
"""Show normalized distance fluctuation matrix using
:func:`~matplotlib.pyplot.imshow`. By default, *origin=lower*
keyword arguments are passed to this function,
but user can overwrite these parameters."""
import math
import matplotlib
import matplotlib.pyplot as plt
normdistfunct = model.getNormDistFluct(coords)
if not 'origin' in kwargs:
kwargs['origin'] = 'lower'
matplotlib.rcParams['font.size'] = '14'
fig = plt.figure(num=None, figsize=(10, 8), dpi=100, facecolor='w')
show = plt.imshow(normdistfunct, *args, **kwargs), plt.colorbar()
plt.clim(math.floor(np.min(normdistfunct[np.nonzero(normdistfunct)])), \
round(np.amax(normdistfunct),1))
plt.title('Normalized Distance Fluctution Matrix')
plt.xlabel('Indices', fontsize='16')
plt.ylabel('Indices', fontsize='16')
if SETTINGS['auto_show']:
showFigure()
return show
def showMechStiff(model, coords, *args, **kwargs):
"""Show mechanical stiffness matrix using :func:`~matplotlib.pyplot.imshow`.
By default, *origin=lower* keyword arguments are passed to this function,
but user can overwrite these parameters."""
import math
import matplotlib
import matplotlib.pyplot as plt
arange = np.arange(model.numAtoms())
model.buildMechStiff(coords)
if not 'origin' in kwargs:
kwargs['origin'] = 'lower'
if 'jet_r' in kwargs:
import matplotlib.cm as plt
kwargs['jet_r'] = 'cmap=cm.jet_r'
MechStiff = model.getStiffness()
matplotlib.rcParams['font.size'] = '14'
fig = plt.figure(num=None, figsize=(10, 8), dpi=100, facecolor='w')
show = plt.imshow(MechStiff, *args, **kwargs), plt.colorbar()
plt.clim(math.floor(np.min(MechStiff[np.nonzero(MechStiff)])), \
round(np.amax(MechStiff),1))
#plt.title('Mechanical Stiffness Matrix')# for {0}'.format(str(model)))
plt.xlabel('Indices', fontsize='16')
plt.ylabel('Indices', fontsize='16')
if SETTINGS['auto_show']:
showFigure()
return show
def showPairDeformationDist(model, coords, ind1, ind2, *args, **kwargs):
"""Show distribution of deformations in distance contributed by each mode
for selected pair of residues *ind1* *ind2*
using :func:`~matplotlib.pyplot.plot`. """
import matplotlib
import matplotlib.pyplot as plt
if not isinstance(model, NMA):
raise TypeError('model must be a NMA instance, '
'not {0}'.format(type(model)))
elif not model.is3d():
raise TypeError('model must be a 3-dimensional NMA instance')
elif len(model) == 0:
raise ValueError('model must have normal modes calculated')
elif model.getStiffness() is None:
raise ValueError('model must have stiffness matrix calculated')
d_pair = calcPairDeformationDist(model, coords, ind1, ind2)
with plt.style.context('fivethirtyeight'):
matplotlib.rcParams['font.size'] = '16'
fig = plt.figure(num=None, figsize=(12, 8), dpi=100, facecolor='w')
#plt.title(str(model))
plt.plot(d_pair[0], d_pair[1], 'k-', linewidth=1.5, *args, **kwargs)
plt.xlabel('mode (k)', fontsize='18')
plt.ylabel('d$^k$' '($\AA$)', fontsize='18')
if SETTINGS['auto_show']:
showFigure()
return plt.show
def showCumulFractVars(modes, *args, **kwargs):
"""Show fraction of variances of *modes* using :func:`~matplotlib.pyplot.
plot`. Note that mode indices are incremented by 1. See also
:func:`.showFractVars` function."""
import matplotlib.pyplot as plt
if not isinstance(modes, (Mode, NMA, ModeSet)):
raise TypeError('modes must be a Mode, NMA, or ModeSet instance, '
'not {0}'.format(type(modes)))
if isinstance(modes, Mode):
indices = modes.getIndices() + 0.5
modes = [modes]
elif isinstance(modes, ModeSet):
indices = modes.getIndices() + 0.5
else:
indices = np.arange(len(modes)) + 0.5
fracts = calcFractVariance(modes).cumsum()
show = plt.plot(indices, fracts, *args, **kwargs)
axis = list(plt.axis())
axis[0] = 0.5
axis[2] = 0
axis[3] = 1
plt.axis(axis)
plt.xlabel('Mode index')
plt.ylabel('Fraction of variance')
if SETTINGS['auto_show']:
showFigure()
return show
def showMode(mode, *args, **kwargs):
"""Show mode array using :func:`~matplotlib.pyplot.plot`."""
import matplotlib.pyplot as plt
show_hinge = kwargs.pop('hinge', True)
show_zero = kwargs.pop('zero', False)
if not isinstance(mode, Mode):
raise TypeError('mode must be a Mode instance, '
'not {0}'.format(type(mode)))
if mode.is3d():
a3d = mode.getArrayNx3()
show = plt.plot(a3d[:, 0], *args, label='x-component', **kwargs)
plt.plot(a3d[:, 1], *args, label='y-component', **kwargs)
plt.plot(a3d[:, 2], *args, label='z-component', **kwargs)
else:
a1d = mode._getArray()
show = plt.plot(a1d, *args, **kwargs)
if show_hinge:
hinges = mode.getHinges()
if hinges is not None:
plt.plot(hinges, a1d[hinges], 'r*')
plt.title(str(mode))
plt.xlabel('Indices')
if show_zero:
plt.plot(plt.xlim(), (0,0), 'r--')
if SETTINGS['auto_show']:
showFigure()
return show
def showMeanMechStiff(model, coords, header, chain='A', *args, **kwargs):
"""Show mean value of effective spring constant with secondary structure
taken from MechStiff. Header is needed to obatin secondary structure range.
Using ``'jet_r'`` as argument color map will be reverse (similar to VMD
program coding).
"""
meanStiff = np.array([np.mean(model.getStiffness(), axis=0)])
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patches as patches
fig = plt.figure(figsize=[18, 6], facecolor='w', dpi=100)
if 'jet_r' in kwargs:
import matplotlib.cm as plt
kwargs['jet_r'] = 'cmap=cm.jet_r'
if 'nearest' in kwargs:
kwargs['nearest'] = 'interpolation=nearest'
with plt.style.context('fivethirtyeight'):
ax = fig.add_subplot(111)
matplotlib.rcParams['font.size'] = '24'
plt.plot(np.arange(len(meanStiff[0])) + coords.getResnums()[0],
meanStiff[0],
'k-',
linewidth=3)
plt.xlim(coords.getResnums()[0], coords.getResnums()[-1])
ax_top = round(
np.max(meanStiff[0]) +
((np.max(meanStiff[0]) - np.min(meanStiff[0])) / 3))
ax_bottom = np.floor(np.min(meanStiff[0]))
LOGGER.info(
'The range of mean effective force constant is: {0} to {1}.'.
format(min(meanStiff[0]), max(meanStiff[0])))
plt.ylim(ax_bottom, ax_top)
plt.xlabel('residue', fontsize='22')
plt.ylabel('mean $\kappa$ [a.u.]', fontsize='22')
ax = fig.add_subplot(411, aspect='equal')
plt.imshow(meanStiff, *args, **kwargs)
header_ss = header['sheet_range'] + header['helix_range']
for i in range(len(header_ss)):
if header_ss[i][1] == chain:
beg = int(header_ss[i][-2]) - coords.getResnums()[0]
end = int(header_ss[i][-1]) - coords.getResnums()[0]
add_beg = end - beg
if header_ss[i][0] == 'H':
ax.add_patch(patches.Rectangle((beg-1,-0.7),add_beg,\
1.4,fill=False, linestyle='solid',edgecolor='#b22683', linewidth=2))
elif header_ss[i][0] == 'E':
if header_ss[i][2] == -1:
ax.add_patch(patches.Arrow(beg-1,0,add_beg,0,width=4.65, \
fill=False, linestyle='solid',edgecolor='black', linewidth=2))
else:
ax.add_patch(patches.Arrow(end-1,0,add_beg*(-1),0,width=4.65, \
fill=False, linestyle='solid',edgecolor='black', linewidth=2))
plt.axis('off')
ax.set_ylim(-1.7, 1.7)
if SETTINGS['auto_show']:
showFigure()
return plt.show
def showOverlapTable(modes_x, modes_y, **kwargs):
"""Show overlap table using :func:`~matplotlib.pyplot.pcolor`. *modes_x*
and *modes_y* are sets of normal modes, and correspond to x and y axes of
the plot. Note that mode indices are incremented by 1. List of modes
is assumed to contain a set of contiguous modes from the same model.
Default arguments for :func:`~matplotlib.pyplot.pcolor`:
* ``cmap=plt.cm.jet``
* ``norm=plt.normalize(0, 1)``"""
import matplotlib.pyplot as plt
overlap = abs(calcOverlap(modes_y, modes_x))
if overlap.ndim == 0:
overlap = np.array([[overlap]])
elif overlap.ndim == 1:
overlap = overlap.reshape((modes_y.numModes(), modes_x.numModes()))
cmap = kwargs.pop('cmap', plt.cm.jet)
norm = kwargs.pop('norm', plt.normalize(0, 1))
show = (plt.pcolor(overlap, cmap=cmap, norm=norm, **kwargs),
plt.colorbar())
x_range = np.arange(1, modes_x.numModes() + 1)
plt.xticks(x_range-0.5, x_range)
plt.xlabel(str(modes_x))
y_range = np.arange(1, modes_y.numModes() + 1)
plt.yticks(y_range-0.5, y_range)
plt.ylabel(str(modes_y))
plt.axis([0, modes_x.numModes(), 0, modes_y.numModes()])
if SETTINGS['auto_show']:
showFigure()
return show
def showMode(mode, *args, **kwargs):
"""Show mode array using :func:`~matplotlib.pyplot.plot`."""
import matplotlib.pyplot as plt
show_hinge = kwargs.pop('hinge', True)
show_zero = kwargs.pop('zero', False)
if not isinstance(mode, Mode):
raise TypeError('mode must be a Mode instance, '
'not {0}'.format(type(mode)))
if mode.is3d():
a3d = mode.getArrayNx3()
show = plt.plot(a3d[:, 0], *args, label='x-component', **kwargs)
plt.plot(a3d[:, 1], *args, label='y-component', **kwargs)
plt.plot(a3d[:, 2], *args, label='z-component', **kwargs)
else:
a1d = mode._getArray()
show = plt.plot(a1d, *args, **kwargs)
if show_hinge:
hinges = mode.getHinges()
if hinges is not None:
plt.plot(hinges, a1d[hinges], 'r*')
plt.title(str(mode))
plt.xlabel('Indices')
if show_zero:
plt.plot(plt.xlim(), (0, 0), 'r--')
if SETTINGS['auto_show']:
showFigure()
return show
def showNormedSqFlucts(modes, *args, **kwargs):
"""Show normalized square fluctuations via :func:`~matplotlib.pyplot.plot`.
"""
import matplotlib.pyplot as plt
sqf = calcSqFlucts(modes)
args = list(args)
modesarg = []
i = 0
while i < len(args):
if isinstance(args[i], (VectorBase, ModeSet, NMA)):
modesarg.append(args.pop(i))
else:
i += 1
show = [plt.plot(sqf/(sqf**2).sum()**0.5, *args,
label='{0}'.format(str(modes)), **kwargs)]
plt.xlabel('Indices')
plt.ylabel('Square fluctuations')
for modes in modesarg:
sqf = calcSqFlucts(modes)
show.append(plt.plot(sqf/(sqf**2).sum()**0.5, *args,
label='{0}'.format(str(modes)), **kwargs))
if SETTINGS['auto_show']:
showFigure()
return show
def showScaledSqFlucts(modes, *args, **kwargs):
"""Show scaled square fluctuations using :func:`~matplotlib.pyplot.plot`.
Modes or mode sets given as additional arguments will be scaled to have
the same mean squared fluctuations as *modes*."""
import matplotlib.pyplot as plt
sqf = calcSqFlucts(modes)
mean = sqf.mean()
args = list(args)
modesarg = []
i = 0
while i < len(args):
if isinstance(args[i], (VectorBase, ModeSet, NMA)):
modesarg.append(args.pop(i))
else:
i += 1
show = [plt.plot(sqf, *args, label=str(modes), **kwargs)]
plt.xlabel('Indices')
plt.ylabel('Square fluctuations')
for modes in modesarg:
sqf = calcSqFlucts(modes)
scalar = mean / sqf.mean()
show.append(
plt.plot(sqf * scalar,
*args,
label='{0} (x{1:.2f})'.format(str(modes), scalar),
**kwargs))
if SETTINGS['auto_show']:
showFigure()
return show
def showOverlap(mode, modes, *args, **kwargs):
"""Show overlap :func:`~matplotlib.pyplot.bar`.
:arg mode: a single mode/vector
:type mode: :class:`.Mode`, :class:`.Vector`
:arg modes: multiple modes
:type modes: :class:`.ModeSet`, :class:`.ANM`, :class:`.GNM`, :class:`.PCA`
"""
import matplotlib.pyplot as plt
if not isinstance(mode, (Mode, Vector)):
raise TypeError('mode must be Mode or Vector, not {0}'
.format(type(mode)))
if not isinstance(modes, (NMA, ModeSet)):
raise TypeError('modes must be NMA or ModeSet, not {0}'
.format(type(modes)))
overlap = abs(calcOverlap(mode, modes))
if isinstance(modes, NMA):
arange = np.arange(0.5, len(modes)+0.5)
else:
arange = modes.getIndices() + 0.5
show = plt.bar(arange, overlap, *args, **kwargs)
plt.title('Overlap with {0}'.format(str(mode)))
plt.xlabel('{0} mode index'.format(modes))
plt.ylabel('Overlap')
if SETTINGS['auto_show']:
showFigure()
return show
def showCumulOverlap(mode, modes, *args, **kwargs):
"""Show cumulative overlap using :func:`~matplotlib.pyplot.plot`.
:type mode: :class:`.Mode`, :class:`.Vector`
:arg modes: multiple modes
:type modes: :class:`.ModeSet`, :class:`.ANM`, :class:`.GNM`, :class:`.PCA`
"""
import matplotlib.pyplot as plt
if not isinstance(mode, (Mode, Vector)):
raise TypeError('mode must be NMA, ModeSet, Mode or Vector, not {0}'
.format(type(mode)))
if not isinstance(modes, (NMA, ModeSet)):
raise TypeError('modes must be NMA, ModeSet, or Mode, not {0}'
.format(type(modes)))
cumov = (calcOverlap(mode, modes) ** 2).cumsum() ** 0.5
if isinstance(modes, NMA):
arange = np.arange(0.5, len(modes)+0.5)
else:
arange = modes.getIndices() + 0.5
show = plt.plot(arange, cumov, *args, **kwargs)
plt.title('Cumulative overlap with {0}'.format(str(mode)))
plt.xlabel('{0} mode index'.format(modes))
plt.ylabel('Cumulative overlap')
plt.axis((arange[0]-0.5, arange[-1]+0.5, 0, 1))
if SETTINGS['auto_show']:
showFigure()
return show
def showMechStiff(model, coords, *args, **kwargs):
"""Show mechanical stiffness matrix using :func:`~matplotlib.pyplot.imshow`.
By default, *origin=lower* keyword arguments are passed to this function,
but user can overwrite these parameters."""
import math
import matplotlib
import matplotlib.pyplot as plt
arange = np.arange(model.numAtoms())
model.buildMechStiff(coords)
if not 'origin' in kwargs:
kwargs['origin'] = 'lower'
if 'jet_r' in kwargs:
import matplotlib.cm as plt
kwargs['jet_r'] = 'cmap=cm.jet_r'
MechStiff = model.getStiffness()
matplotlib.rcParams['font.size'] = '14'
fig = plt.figure(num=None, figsize=(10,8), dpi=100, facecolor='w')
show = plt.imshow(MechStiff, *args, **kwargs), plt.colorbar()
plt.clim(math.floor(np.min(MechStiff[np.nonzero(MechStiff)])), \
round(np.amax(MechStiff),1))
#plt.title('Mechanical Stiffness Matrix')# for {0}'.format(str(model)))
plt.xlabel('Indices', fontsize='16')
plt.ylabel('Indices', fontsize='16')
if SETTINGS['auto_show']:
showFigure()
return show
def showCumulOverlap(mode, modes, *args, **kwargs):
"""Show cumulative overlap using :func:`~matplotlib.pyplot.plot`.
:type mode: :class:`.Mode`, :class:`.Vector`
:arg modes: multiple modes
:type modes: :class:`.ModeSet`, :class:`.ANM`, :class:`.GNM`, :class:`.PCA`
"""
import matplotlib.pyplot as plt
if not isinstance(mode, (Mode, Vector)):
raise TypeError(
'mode must be NMA, ModeSet, Mode or Vector, not {0}'.format(
type(mode)))
if not isinstance(modes, (NMA, ModeSet)):
raise TypeError('modes must be NMA, ModeSet, or Mode, not {0}'.format(
type(modes)))
cumov = (calcOverlap(mode, modes)**2).cumsum()**0.5
if isinstance(modes, NMA):
arange = np.arange(0.5, len(modes) + 0.5)
else:
arange = modes.getIndices() + 0.5
show = plt.plot(arange, cumov, *args, **kwargs)
plt.title('Cumulative overlap with {0}'.format(str(mode)))
plt.xlabel('{0} mode index'.format(modes))
plt.ylabel('Cumulative overlap')
plt.axis((arange[0] - 0.5, arange[-1] + 0.5, 0, 1))
if SETTINGS['auto_show']:
showFigure()
return show
def showCumulFractVars(modes, *args, **kwargs):
"""Show fraction of variances of *modes* using :func:`~matplotlib.pyplot.
plot`. Note that mode indices are incremented by 1. See also
:func:`.showFractVars` function."""
import matplotlib.pyplot as plt
if not isinstance(modes, (Mode, NMA, ModeSet)):
raise TypeError('modes must be a Mode, NMA, or ModeSet instance, '
'not {0}'.format(type(modes)))
if isinstance(modes, Mode):
indices = modes.getIndices() + 0.5
modes = [modes]
elif isinstance(modes, ModeSet):
indices = modes.getIndices() + 0.5
else:
indices = np.arange(len(modes)) + 0.5
fracts = calcFractVariance(modes).cumsum()
show = plt.plot(indices, fracts, *args, **kwargs)
axis = list(plt.axis())
axis[0] = 0.5
axis[2] = 0
axis[3] = 1
plt.axis(axis)
plt.xlabel('Mode index')
plt.ylabel('Fraction of variance')
if SETTINGS['auto_show']:
showFigure()
return show
def showOverlap(mode, modes, *args, **kwargs):
"""Show overlap :func:`~matplotlib.pyplot.bar`.
:arg mode: a single mode/vector
:type mode: :class:`.Mode`, :class:`.Vector`
:arg modes: multiple modes
:type modes: :class:`.ModeSet`, :class:`.ANM`, :class:`.GNM`, :class:`.PCA`
"""
import matplotlib.pyplot as plt
if not isinstance(mode, (Mode, Vector)):
raise TypeError('mode must be Mode or Vector, not {0}'.format(
type(mode)))
if not isinstance(modes, (NMA, ModeSet)):
raise TypeError('modes must be NMA or ModeSet, not {0}'.format(
type(modes)))
overlap = abs(calcOverlap(mode, modes))
if isinstance(modes, NMA):
arange = np.arange(0.5, len(modes) + 0.5)
else:
arange = modes.getIndices() + 0.5
show = plt.bar(arange, overlap, *args, **kwargs)
plt.title('Overlap with {0}'.format(str(mode)))
plt.xlabel('{0} mode index'.format(modes))
plt.ylabel('Overlap')
if SETTINGS['auto_show']:
showFigure()
return show
def showSignatureVariances(mode_ensemble, **kwargs):
"""
Show the distribution of signature variances using
:func:`~matplotlib.pyplot.hist`.
"""
from matplotlib.pyplot import figure, hist, annotate, legend, xlabel, ylabel
from matplotlib.figure import Figure
fig = kwargs.pop('figure', None)
if isinstance(fig, Figure):
fig_num = fig.number
elif fig is None or isinstance(fig, (int, str)):
fig_num = fig
else:
raise TypeError('figure can be either an instance of matplotlib.figure.Figure '
'or a figure number.')
if SETTINGS['auto_show']:
figure(fig_num)
elif fig_num is not None:
figure(fig_num)
fract = kwargs.pop('fraction', True)
show_legend = kwargs.pop('legend', True)
if fract:
sig = calcSignatureFractVariance(mode_ensemble)
else:
sig = mode_ensemble.getVariances()
W = sig.getArray()[:, ::-1] # reversed to accommodate with matplotlib.pyplot.hist
weights = np.ones_like(W)/float(len(W))
indices = mode_ensemble.getIndices()[0]
legends = ['mode %d'%(i+1) for i in indices][::-1]
bins = kwargs.pop('bins', 'auto')
if bins == 'auto':
_, bins = np.histogram(W.flatten(), bins='auto')
elif np.isscalar(bins) and isinstance(bins, (int, np.integer)):
step = (W.max() - W.min())/bins
bins = np.arange(W.min(), W.max(), step)
histtype = kwargs.pop('histtype', 'stepfilled')
label = kwargs.pop('label', legends)
weights = kwargs.pop('weights', weights)
n, bins, patches = hist(W, bins=bins, weights=weights,
histtype=histtype, label=label, **kwargs)
if show_legend:
legend()
xlabel('Variance')
ylabel('Probability')
if SETTINGS['auto_show']:
showFigure()
return n, bins, patches
def showMeanMechStiff(model, coords, header, chain='A', *args, **kwargs):
"""Show mean value of effective spring constant with secondary structure
taken from MechStiff. Header is needed to obatin secondary structure range.
Using ``'jet_r'`` as argument color map will be reverse (similar to VMD
program coding).
"""
meanStiff = np.array([np.mean(model.getStiffness(), axis=0)])
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patches as patches
fig=plt.figure(figsize=[18,6], facecolor='w', dpi=100)
if 'jet_r' in kwargs:
import matplotlib.cm as plt
kwargs['jet_r'] = 'cmap=cm.jet_r'
if 'nearest' in kwargs:
kwargs['nearest'] = 'interpolation=nearest'
with plt.style.context('fivethirtyeight'):
ax = fig.add_subplot(111)
matplotlib.rcParams['font.size'] = '24'
plt.plot(np.arange(len(meanStiff[0]))+coords.getResnums()[0],meanStiff[0], 'k-', linewidth = 3)
plt.xlim(coords.getResnums()[0], coords.getResnums()[-1])
ax_top=round(np.max(meanStiff[0])+((np.max(meanStiff[0])-np.min(meanStiff[0]))/3))
ax_bottom=np.floor(np.min(meanStiff[0]))
LOGGER.info('The range of mean effective force constant is: {0} to {1}.'
.format(min(meanStiff[0]), max(meanStiff[0])))
plt.ylim(ax_bottom,ax_top)
plt.xlabel('residue', fontsize = '22')
plt.ylabel('mean $\kappa$ [a.u.]', fontsize = '22')
ax = fig.add_subplot(411, aspect='equal')
plt.imshow(meanStiff, *args, **kwargs)
header_ss = header['sheet_range'] + header['helix_range']
for i in range(len(header_ss)):
if header_ss[i][1] == chain:
beg = int(header_ss[i][-2])-coords.getResnums()[0]
end = int(header_ss[i][-1])-coords.getResnums()[0]
add_beg = end - beg
if header_ss[i][0] == 'H':
ax.add_patch(patches.Rectangle((beg-1,-0.7),add_beg,\
1.4,fill=False, linestyle='solid',edgecolor='#b22683', linewidth=2))
elif header_ss[i][0] == 'E':
if header_ss[i][2] == -1:
ax.add_patch(patches.Arrow(beg-1,0,add_beg,0,width=4.65, \
fill=False, linestyle='solid',edgecolor='black', linewidth=2))
else:
ax.add_patch(patches.Arrow(end-1,0,add_beg*(-1),0,width=4.65, \
fill=False, linestyle='solid',edgecolor='black', linewidth=2))
plt.axis('off')
ax.set_ylim(-1.7,1.7)
if SETTINGS['auto_show']:
showFigure()
return plt.show
def showSqFlucts(modes, *args, **kwargs):
"""Show square fluctuations using :func:`~matplotlib.pyplot.plot`. See
also :func:`.calcSqFlucts`."""
import matplotlib.pyplot as plt
sqf = calcSqFlucts(modes)
if not 'label' in kwargs:
kwargs['label'] = str(modes)
show = plt.plot(sqf, *args, **kwargs)
plt.xlabel('Indices')
plt.ylabel('Square fluctuations')
plt.title(str(modes))
if SETTINGS['auto_show']:
showFigure()
return show
def showSqFlucts(modes, *args, **kwargs):
"""Show square fluctuations using :func:`~matplotlib.pyplot.plot`. See
also :func:`.calcSqFlucts`."""
import matplotlib.pyplot as plt
sqf = calcSqFlucts(modes)
if not 'label' in kwargs:
kwargs['label'] = str(modes)
show = plt.plot(sqf, *args, **kwargs)
plt.xlabel('Indices')
plt.ylabel('Square fluctuations')
plt.title(str(modes))
if SETTINGS['auto_show']:
showFigure()
return show
def showContactMap(enm, *args, **kwargs):
"""Show Kirchhoff matrix using :func:`~matplotlib.pyplot.spy`."""
import matplotlib.pyplot as plt
if not isinstance(enm, GNMBase):
raise TypeError('model argument must be an ENM instance')
kirchhoff = enm.getKirchhoff()
if kirchhoff is None:
LOGGER.warning('kirchhoff matrix is not set')
return None
show = plt.spy(kirchhoff, *args, **kwargs)
plt.title('{0} contact map'.format(enm.getTitle()))
plt.xlabel('Residue index')
plt.ylabel('Residue index')
if SETTINGS['auto_show']:
showFigure()
return show
def showContactMap(enm, *args, **kwargs):
"""Show Kirchhoff matrix using :func:`~matplotlib.pyplot.spy`."""
import matplotlib.pyplot as plt
if not isinstance(enm, GNMBase):
raise TypeError('model argument must be an ENM instance')
kirchhoff = enm.getKirchhoff()
if kirchhoff is None:
LOGGER.warning('kirchhoff matrix is not set')
return None
show = plt.spy(kirchhoff, *args, **kwargs)
plt.title('{0} contact map'.format(enm.getTitle()))
plt.xlabel('Residue index')
plt.ylabel('Residue index')
if SETTINGS['auto_show']:
showFigure()
return show
def showDiffMatrix(matrix1, matrix2, *args, **kwargs):
"""Show the difference between two cross-correlation matrices from
different models. For given *matrix1* and *matrix2* show the difference
between them in the form of (matrix2 - matrix1) and plot the difference
matrix using :func:`~matplotlib.pyplot.imshow`. When :class:`.NMA` models
are passed instead of matrices, the functions could call
:func:`.calcCrossCorr` function to calculate the matrices for given modes.
To display the absolute values in the difference matrix, user could set
*abs* keyword argument **True**.
By default, *origin=lower* and *interpolation=bilinear* keyword arguments
are passed to this function, but user can overwrite these parameters.
"""
import matplotlib.pyplot as plt
try:
dim1, shape1 = matrix1.ndim, matrix1.shape
except AttributeError:
matrix1 = calcCrossCorr(matrix1)
dim1, shape1 = matrix1.ndim, matrix1.shape
try:
dim2, shape2 = matrix2.ndim, matrix2.shape
except AttributeError:
matrix2 = calcCrossCorr(matrix2)
dim2, shape2 = matrix2.ndim, matrix2.shape
if not ((dim1 == dim2 == 2) and (shape1 == shape2)):
raise ValueError("Matrices must have same square shape.")
if shape1[0] * shape1[1] == 0:
raise ValueError("There are no data in matrices.")
diff = matrix2 - matrix1
if not "interpolation" in kwargs:
kwargs["interpolation"] = "bilinear"
if not "origin" in kwargs:
kwargs["origin"] = "lower"
if kwargs.pop("abs", False):
diff = np.abs(diff)
show = plt.imshow(diff, *args, **kwargs), plt.colorbar()
plt.axis([-0.5, shape1[1] - 0.5, -0.5, shape1[0] - 0.5])
plt.title("Difference Matrix")
if SETTINGS["auto_show"]:
showFigure()
plt.xlabel("Indices")
plt.ylabel("Indices")
return show
def showDiffMatrix(matrix1, matrix2, *args, **kwargs):
"""Show the difference between two cross-correlation matrices from
different models. For given *matrix1* and *matrix2* show the difference
between them in the form of (matrix2 - matrix1) and plot the difference
matrix using :func:`~matplotlib.pyplot.imshow`. When :class:`.NMA` models
are passed instead of matrices, the functions could call
:func:`.calcCrossCorr` function to calculate the matrices for given modes.
To display the absolute values in the difference matrix, user could set
*abs* keyword argument **True**.
By default, *origin=lower* and *interpolation=bilinear* keyword arguments
are passed to this function, but user can overwrite these parameters.
"""
import matplotlib.pyplot as plt
try:
dim1, shape1 = matrix1.ndim, matrix1.shape
except AttributeError:
matrix1 = calcCrossCorr(matrix1)
dim1, shape1 = matrix1.ndim, matrix1.shape
try:
dim2, shape2 = matrix2.ndim, matrix2.shape
except AttributeError:
matrix2 = calcCrossCorr(matrix2)
dim2, shape2 = matrix2.ndim, matrix2.shape
if (not ((dim1 == dim2 == 2) and (shape1 == shape2))):
raise ValueError('Matrices must have same square shape.')
if shape1[0] * shape1[1] == 0:
raise ValueError('There are no data in matrices.')
diff = matrix2 - matrix1
if not 'interpolation' in kwargs:
kwargs['interpolation'] = 'bilinear'
if not 'origin' in kwargs:
kwargs['origin'] = 'lower'
if kwargs.pop('abs', False):
diff = np.abs(diff)
show = plt.imshow(diff, *args, **kwargs), plt.colorbar()
plt.axis([-.5, shape1[1] - .5, -.5, shape1[0] - .5])
plt.title('Difference Matrix')
if SETTINGS['auto_show']:
showFigure()
plt.xlabel('Indices')
plt.ylabel('Indices')
return show
def showDomains(domains, linespec='-', **kwargs):
"""A convenient function that can be used to visualize Hi-C structural domains.
*kwargs* will be passed to :func:`matplotlib.pyplot.plot`.
:arg domains: a 2D array of Hi-C domains, such as [[start1, end1], [start2, end2], ...].
:type domains: :class:`numpy.ndarray`
"""
domains = np.array(domains)
shape = domains.shape
if len(shape) < 2:
# convert to domain list if labels are provided
indicators = np.diff(domains)
indicators = np.append(1., indicators)
indicators[-1] = 1
sites = np.where(indicators != 0)[0]
starts = sites[:-1]
ends = sites[1:]
domains = np.array([starts, ends]).T
from matplotlib.pyplot import figure, plot
x = []
y = []
lwd = kwargs.pop('linewidth', 1)
lwd = kwargs.pop('lw', lwd)
linewidth = np.abs(lwd)
for i in range(len(domains)):
domain = domains[i]
start = domain[0]
end = domain[1]
if lwd > 0:
x.extend([start, end, end])
y.extend([start, start, end])
else:
x.extend([start, start, end])
y.extend([start, end, end])
plt = plot(x, y, linespec, linewidth=linewidth, **kwargs)
if SETTINGS['auto_show']:
showFigure()
return plt
def showMode(mode, *args, **kwargs):
"""Show mode array using :func:`~matplotlib.pyplot.plot`."""
import matplotlib.pyplot as plt
if not isinstance(mode, Mode):
raise TypeError('mode must be a Mode instance, '
'not {0}'.format(type(mode)))
if mode.is3d():
a3d = mode.getArrayNx3()
show = plt.plot(a3d[:, 0], *args, label='x-component', **kwargs)
plt.plot(a3d[:, 1], *args, label='y-component', **kwargs)
plt.plot(a3d[:, 2], *args, label='z-component', **kwargs)
else:
show = plt.plot(mode._getArray(), *args, **kwargs)
plt.title(str(mode))
plt.xlabel('Indices')
if SETTINGS['auto_show']:
showFigure()
return show
def showMode(mode, *args, **kwargs):
"""Show mode array using :func:`~matplotlib.pyplot.plot`."""
import matplotlib.pyplot as plt
if not isinstance(mode, Mode):
raise TypeError('mode must be a Mode instance, '
'not {0}'.format(type(mode)))
if mode.is3d():
a3d = mode.getArrayNx3()
show = plt.plot(a3d[:, 0], *args, label='x-component', **kwargs)
plt.plot(a3d[:, 1], *args, label='y-component', **kwargs)
plt.plot(a3d[:, 2], *args, label='z-component', **kwargs)
else:
show = plt.plot(mode._getArray(), *args, **kwargs)
plt.title(str(mode))
plt.xlabel('Indices')
if SETTINGS['auto_show']:
showFigure()
return show
def showSqFlucts(modes, *args, **kwargs):
"""Show square fluctuations using :func:`~matplotlib.pyplot.plot`. See
also :func:`.calcSqFlucts`."""
import matplotlib.pyplot as plt
show_hinge = kwargs.pop('hinge', True)
sqf = calcSqFlucts(modes)
if not 'label' in kwargs:
kwargs['label'] = str(modes)
show = plt.plot(sqf, *args, **kwargs)
plt.xlabel('Indices')
plt.ylabel('Square fluctuations')
plt.title(str(modes))
if show_hinge and not modes.is3d():
hinges = modes.getHinges()
if hinges is not None:
plt.plot(hinges, sqf[hinges], 'r*')
if SETTINGS['auto_show']:
showFigure()
return show
def showSqFlucts(modes, *args, **kwargs):
"""Show square fluctuations using :func:`~matplotlib.pyplot.plot`. See
also :func:`.calcSqFlucts`."""
import matplotlib.pyplot as plt
show_hinge = kwargs.pop('hinge', True)
sqf = calcSqFlucts(modes)
if not 'label' in kwargs:
kwargs['label'] = str(modes)
show = plt.plot(sqf, *args, **kwargs)
plt.xlabel('Indices')
plt.ylabel('Square fluctuations')
plt.title(str(modes))
if show_hinge and not modes.is3d():
hinges = modes.getHinges()
if hinges is not None:
plt.plot(hinges, sqf[hinges], 'r*')
if SETTINGS['auto_show']:
showFigure()
return show
def showOccupancies(pdbensemble, *args, **kwargs):
"""Show occupancies for the PDB ensemble using :func:`~matplotlib.pyplot.
plot`. Occupancies are calculated using :meth:`calcOccupancies`."""
import matplotlib.pyplot as plt
if not isinstance(pdbensemble, PDBEnsemble):
raise TypeError('pdbensemble must be a PDBEnsemble instance')
weights = calcOccupancies(pdbensemble)
if weights is None:
return None
show = plt.plot(weights, *args, **kwargs)
axis = list(plt.axis())
axis[2] = 0
axis[3] += 1
plt.axis(axis)
plt.xlabel('Atom index')
plt.ylabel('Sum of weights')
if SETTINGS['auto_show']:
showFigure()
return show
def showOccupancies(pdbensemble, *args, **kwargs):
"""Show occupancies for the PDB ensemble using :func:`~matplotlib.pyplot.
plot`. Occupancies are calculated using :meth:`calcOccupancies`."""
import matplotlib.pyplot as plt
if not isinstance(pdbensemble, PDBEnsemble):
raise TypeError('pdbensemble must be a PDBEnsemble instance')
weights = calcOccupancies(pdbensemble)
if weights is None:
return None
show = plt.plot(weights, *args, **kwargs)
axis = list(plt.axis())
axis[2] = 0
axis[3] += 1
plt.axis(axis)
plt.xlabel('Atom index')
plt.ylabel('Sum of weights')
if SETTINGS['auto_show']:
showFigure()
return show
def showDomains(domains, linespec='r-', **kwargs):
"""A convenient function that can be used to visualize Hi-C structural domains.
*kwargs* will be passed to :func:`matplotlib.pyplot.plot`.
:arg domains: a 2D array of Hi-C domains, such as [[start1, end1], [start2, end2], ...].
:type domains: :class:`numpy.ndarray`
"""
domains = np.array(domains)
shape = domains.shape
if len(shape) < 2:
# convert to domain list if labels are provided
indicators = np.diff(domains)
indicators = np.append(1., indicators)
indicators[-1] = 1
sites = np.where(indicators != 0)[0]
starts = sites[:-1]
ends = sites[1:]
domains = np.array([starts, ends]).T
from matplotlib.pyplot import figure, plot
x = []; y = []
lwd = kwargs.pop('linewidth', 1)
linewidth = np.abs(lwd)
for i in range(len(domains)):
domain = domains[i]
start = domain[0]; end = domain[1]
if lwd > 0:
x.extend([start, end, end])
y.extend([start, start, end])
else:
x.extend([start, start, end])
y.extend([start, end, end])
plt = plot(x, y, linespec, linewidth=linewidth, **kwargs)
if SETTINGS['auto_show']:
showFigure()
return plt
def showMap(map, spec='', **kwargs):
"""A convenient function that can be used to visualize Hi-C contact map.
*kwargs* will be passed to :func:`matplotlib.pyplot.imshow`.
:arg map: a Hi-C contact map.
:type map: :class:`numpy.ndarray`
:arg spec: a string specifies how to preprocess the matrix. Blank for no preprocessing,
'p' for showing only data from *p*-th to *100-p*-th percentile. '_' is to suppress
creating a new figure and paint to the current one instead. The letter specifications
can be applied sequentially, e.g. 'p_'.
:type spec: str
:arg p: specifies the percentile threshold.
:type p: double
"""
assert isinstance(map, np.ndarray), 'map must be a numpy.ndarray.'
from matplotlib.pyplot import figure, imshow
if not '_' in spec:
figure()
if 'p' in spec:
p = kwargs.pop('p', 5)
lp = kwargs.pop('lp', p)
hp = kwargs.pop('hp', 100 - p)
vmin = np.percentile(map, lp)
vmax = np.percentile(map, hp)
else:
vmin = vmax = None
im = imshow(map, vmin=vmin, vmax=vmax, **kwargs)
if SETTINGS['auto_show']:
showFigure()
return im
def showFractVars(modes, *args, **kwargs):
"""Show fraction of variances using :func:`~matplotlib.pyplot.bar`. Note
that mode indices are incremented by 1."""
import matplotlib.pyplot as plt
if not isinstance(modes, (ModeSet, NMA)):
raise TypeError('modes must be NMA, or ModeSet, not {0}'.format(
type(modes)))
fracts = calcFractVariance(modes)
fracts = [(int(mode), fract) for mode, fract in zip(modes, fracts)]
fracts = np.array(fracts)
show = plt.bar(fracts[:, 0] + 0.5, fracts[:, 1], *args, **kwargs)
axis = list(plt.axis())
axis[0] = 0.5
axis[2] = 0
axis[3] = 1
plt.axis(axis)
plt.xlabel('Mode index')
plt.ylabel('Fraction of variance')
if SETTINGS['auto_show']:
showFigure()
return show
def showFractVars(modes, *args, **kwargs):
"""Show fraction of variances using :func:`~matplotlib.pyplot.bar`. Note
that mode indices are incremented by 1."""
import matplotlib.pyplot as plt
if not isinstance(modes, (ModeSet, NMA)):
raise TypeError('modes must be NMA, or ModeSet, not {0}'
.format(type(modes)))
fracts = calcFractVariance(modes)
fracts = [(int(mode), fract) for mode, fract in zip(modes, fracts)]
fracts = np.array(fracts)
show = plt.bar(fracts[:,0]+0.5, fracts[:,1], *args, **kwargs)
axis = list(plt.axis())
axis[0] = 0.5
axis[2] = 0
axis[3] = 1
plt.axis(axis)
plt.xlabel('Mode index')
plt.ylabel('Fraction of variance')
if SETTINGS['auto_show']:
showFigure()
return show
def showCrossCorr(modes, *args, **kwargs):
"""Show cross-correlations using :func:`~matplotlib.pyplot.imshow`. By
default, *origin=lower* and *interpolation=bilinear* keyword arguments
are passed to this function, but user can overwrite these parameters.
See also :func:`.calcCrossCorr`."""
import matplotlib.pyplot as plt
arange = np.arange(modes.numAtoms())
cross_correlations = np.zeros((arange[-1]+2, arange[-1]+2))
cross_correlations[arange[0]+1:,
arange[0]+1:] = calcCrossCorr(modes)
if not 'interpolation' in kwargs:
kwargs['interpolation'] = 'bilinear'
if not 'origin' in kwargs:
kwargs['origin'] = 'lower'
show = plt.imshow(cross_correlations, *args, **kwargs), plt.colorbar()
plt.axis([arange[0]+0.5, arange[-1]+1.5, arange[0]+0.5, arange[-1]+1.5])
plt.title('Cross-correlations for {0}'.format(str(modes)))
plt.xlabel('Indices')
plt.ylabel('Indices')
if SETTINGS['auto_show']:
showFigure()
return show
def showCrossCorr(modes, *args, **kwargs):
"""Show cross-correlations using :func:`~matplotlib.pyplot.imshow`. By
default, *origin=lower* and *interpolation=bilinear* keyword arguments
are passed to this function, but user can overwrite these parameters.
See also :func:`.calcCrossCorr`."""
import matplotlib.pyplot as plt
arange = np.arange(modes.numAtoms())
cross_correlations = np.zeros((arange[-1] + 2, arange[-1] + 2))
cross_correlations[arange[0] + 1:, arange[0] + 1:] = calcCrossCorr(modes)
if not 'interpolation' in kwargs:
kwargs['interpolation'] = 'bilinear'
if not 'origin' in kwargs:
kwargs['origin'] = 'lower'
show = plt.imshow(cross_correlations, *args, **kwargs), plt.colorbar()
plt.axis(
[arange[0] + 0.5, arange[-1] + 1.5, arange[0] + 0.5, arange[-1] + 1.5])
plt.title('Cross-correlations for {0}'.format(str(modes)))
plt.xlabel('Indices')
plt.ylabel('Indices')
if SETTINGS['auto_show']:
showFigure()
return show
def showLinkage(V, **kwargs):
"""Shows the dendrogram of hierarchical clustering on *V*. See :func:`scipy.cluster.hierarchy.dendrogram` for details.
:arg V: row-normalized eigenvectors for the purpose of clustering.
:type V: :class:`numpy.ndarray`
"""
V, _ = _getEigvecs(V, row_norm=True, remove_zero_rows=True)
try:
from scipy.cluster.hierarchy import linkage, dendrogram
except ImportError:
raise ImportError('Use of this function (showLinkage) requires the '
'installation of scipy.')
method = kwargs.pop('method', 'single')
metric = kwargs.pop('metric', 'euclidean')
Z = linkage(V, method=method, metric=metric)
no_labels = kwargs.pop('no_labels', True)
dendrogram(Z, no_labels=no_labels, **kwargs)
if SETTINGS['auto_show']:
showFigure()
return Z
def showProtein(*atoms, **kwargs):
"""Show protein representation using :meth:`~mpl_toolkits.mplot3d.Axes3D`.
This function is designed for generating a quick view of the contents of a
:class:`~.AtomGroup` or :class:`~.Selection`.
Protein atoms matching ``"calpha"`` selection are displayed using solid
lines by picking a random and unique color per chain. Line with can
be adjusted using *lw* argument, e.g. ``lw=12``. Default width is 4.
Chain colors can be overwritten using chain identifier as in ``A='green'``.
Water molecule oxygen atoms are represented by red colored circles. Color
can be changed using *water* keyword argument, e.g. ``water='aqua'``.
Water marker and size can be changed using *wmarker* and *wsize* keywords,
defaults values are ``wmarker='.', wsize=6``.
Hetero atoms matching ``"hetero and noh"`` selection are represented by
circles and unique colors are picked at random on a per residue basis.
Colors can be customized using residue name as in ``NAH='purple'``. Note
that this will color all distinct residues with the same name in the same
color. Hetero atom marker and size can be changed using *hmarker* and
*hsize* keywords, default values are ``hmarker='o', hsize=6``.
ProDy will set the size of axis so the representation is not distorted when
the shape of figure window is close to a square. Colors are picked at
random, except for water oxygens which will always be colored red."""
alist = atoms
for atoms in alist:
if not isinstance(atoms, Atomic):
raise TypeError('atoms must be an Atomic instance')
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
cf = plt.gcf()
show = None
for child in cf.get_children():
if isinstance(child, Axes3D):
show = child
break
if show is None:
show = Axes3D(cf)
from matplotlib import colors
cnames = dict(colors.cnames)
wcolor = kwargs.get('water', 'red').lower()
avoid = np.array(colors.hex2color(cnames.pop(wcolor, cnames.pop('red'))))
for cn, val in cnames.items(): # PY3K: OK
clr = np.array(colors.hex2color(val))
if clr.sum() > 2.4:
cnames.pop(cn)
elif np.abs(avoid - clr).sum() <= 0.6:
cnames.pop(cn)
cnames = list(cnames)
import random
random.shuffle(cnames)
min_ = list()
max_ = list()
for atoms in alist:
if isinstance(atoms, AtomGroup):
title = atoms.getTitle()
else:
title = atoms.getAtomGroup().getTitle()
calpha = atoms.select('calpha')
if calpha:
for ch in HierView(calpha, chain=True):
xyz = ch._getCoords()
chid = ch.getChid()
show.plot(xyz[:, 0], xyz[:, 1], xyz[:, 2],
label=title + '_' + chid,
color=kwargs.get(chid, cnames.pop()).lower(),
lw=kwargs.get('lw', 4))
water = atoms.select('water and noh')
if water:
xyz = atoms.select('water')._getCoords()
show.plot(xyz[:, 0], xyz[:, 1], xyz[:, 2], label=title + '_water',
color=wcolor,
ls='None', marker=kwargs.get('wmarker', '.'),
ms=kwargs.get('wsize', 6))
hetero = atoms.select('not protein and not nucleic and not water')
if hetero:
for res in HierView(hetero).iterResidues():
xyz = res._getCoords()
resname = res.getResname()
resnum = str(res.getResnum())
chid = res.getChid()
show.plot(xyz[:, 0], xyz[:, 1], xyz[:, 2], ls='None',
color=kwargs.get(resname, cnames.pop()).lower(),
label=title + '_' + chid + '_' + resname + resnum,
marker=kwargs.get('hmarker', 'o'),
ms=kwargs.get('hsize', 6))
xyz = atoms._getCoords()
min_.append(xyz.min(0))
max_.append(xyz.max(0))
show.set_xlabel('x')
show.set_ylabel('y')
show.set_zlabel('z')
min_ = np.array(min_).min(0)
max_ = np.array(max_).max(0)
center = (max_ + min_) / 2
half = (max_ - min_).max() / 2
show.set_xlim3d(center[0]-half, center[0]+half)
show.set_ylim3d(center[1]-half, center[1]+half)
show.set_zlim3d(center[2]-half, center[2]+half)
if kwargs.get('legend', False):
show.legend(prop={'size': 10})
if SETTINGS['auto_show']:
showFigure()
return show
def showEllipsoid(modes, onto=None, n_std=2, scale=1., *args, **kwargs):
"""Show an ellipsoid using :meth:`~mpl_toolkits.mplot3d.Axes3D
.plot_wireframe`.
Ellipsoid volume gives an analytical view of the conformational space that
given modes describe.
:arg modes: 3 modes for which ellipsoid will be drawn.
:type modes: :class:`.ModeSet`, :class:`.PCA`, :class:`.ANM`, :class:`.NMA`
:arg onto: 3 modes onto which ellipsoid will be projected.
:type modes: :class:`.ModeSet`, :class:`.PCA`, :class:`.ANM`, :class:`.NMA`
:arg n_std: Number of standard deviations to scale the ellipsoid.
:type n_std: float
:arg scale: Used for scaling the volume of ellipsoid. This can be
obtained from :func:`.sampleModes`.
:type scale: float"""
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
if not isinstance(modes, (NMA, ModeSet)):
raise TypeError('modes must be a NMA or ModeSet instance, '
'not {0}'.format(type(modes)))
if not modes.is3d():
raise ValueError('modes must be from a 3-dimensional model')
if len(modes) != 3:
raise ValueError('length of modes is not equal to 3')
if onto is not None:
if not isinstance(onto, (NMA, ModeSet)):
raise TypeError('onto must be a NMA or ModeSet instance, '
'not {0}'.format(type(onto)))
if not onto.is3d():
raise ValueError('onto must be from a 3-dimensional model')
if len(onto) != 3:
raise ValueError('length of onto is not equal to 3')
if onto.numAtoms() != modes.numAtoms():
raise ValueError('modes and onto must have same number of atoms')
u = np.linspace(0, 2 * np.pi, 100)
v = np.linspace(0, np.pi, 100)
var = modes.getVariances()
#randn = np.random.standard_normal((1000, 3))
#coef = ((randn ** 2 * var).sum(1) ** 0.5).mean()
#scale=float(n_std)*modes.numAtoms()**.5 * float(rmsd) / coef * var **.5
scale = float(n_std) * scale * var**0.5
#scale=float(n_std)*modes.numAtoms()**.5*float(rmsd)/var.sum()**.5*var**.5
x = scale[0] * np.outer(np.cos(u), np.sin(v))
y = scale[1] * np.outer(np.sin(u), np.sin(v))
z = scale[2] * np.outer(np.ones(np.size(u)), np.cos(v))
if onto is not None:
change_of_basis = np.dot(modes._getArray().T, onto._getArray())
xyz = np.array([x.flatten(), y.flatten(), z.flatten()])
xyz = np.dot(xyz.T, change_of_basis)
x = xyz[:, 0].reshape((100, 100))
y = xyz[:, 1].reshape((100, 100))
z = xyz[:, 2].reshape((100, 100))
cf = plt.gcf()
show = None
for child in cf.get_children():
if isinstance(child, Axes3D):
show = child
break
if show is None:
show = Axes3D(cf)
show.plot_wireframe(x, y, z, rstride=6, cstride=6, *args, **kwargs)
if onto is not None:
onto = list(onto)
show.set_xlabel('Mode {0} coordinate'.format(int(onto[0]) + 1))
show.set_ylabel('Mode {0} coordinate'.format(int(onto[1]) + 1))
show.set_zlabel('Mode {0} coordinate'.format(int(onto[2]) + 1))
else:
modes = list(modes)
show.set_xlabel('Mode {0} coordinate'.format(int(modes[0]) + 1))
show.set_ylabel('Mode {0} coordinate'.format(int(modes[1]) + 1))
show.set_zlabel('Mode {0} coordinate'.format(int(modes[2]) + 1))
if SETTINGS['auto_show']:
showFigure()
return show
def showProtein(*atoms, **kwargs):
"""Show protein representation using :meth:`~mpl_toolkits.mplot3d.Axes3D`.
This function is designed for generating a quick view of the contents of a
:class:`~.AtomGroup` or :class:`~.Selection`.
Protein atoms matching ``"calpha"`` selection are displayed using solid
lines by picking a random and unique color per chain. Line with can
be adjusted using *lw* argument, e.g. ``lw=12``. Default width is 4.
Chain colors can be overwritten using chain identifier as in ``A='green'``.
Water molecule oxygen atoms are represented by red colored circles. Color
can be changed using *water* keyword argument, e.g. ``water='aqua'``.
Water marker and size can be changed using *wmarker* and *wsize* keywords,
defaults values are ``wmarker='.', wsize=6``.
Hetero atoms matching ``"hetero and noh"`` selection are represented by
circles and unique colors are picked at random on a per residue basis.
Colors can be customized using residue name as in ``NAH='purple'``. Note
that this will color all distinct residues with the same name in the same
color. Hetero atom marker and size can be changed using *hmarker* and
*hsize* keywords, default values are ``hmarker='o', hsize=6``.
ProDy will set the size of axis so the representation is not distorted when
the shape of figure window is close to a square. Colors are picked at
random, except for water oxygens which will always be colored red.
*** Interactive 3D Rendering in Jupyter Notebook ***
If py3Dmol has been imported then it will be used instead to display
an interactive viewer. See :func:`view3D`
"""
from prody.dynamics.mode import Mode
method = kwargs.pop('draw', None)
modes = kwargs.get('mode', None)
scale = kwargs.get('scale', 100)
# modes need to be specifically a list or a tuple (cannot be an array)
if modes is None:
n_modes = 0
else:
modes = wrapModes(modes)
n_modes = len(modes)
if method is None:
import sys
if 'py3Dmol' in sys.modules:
method = 'py3Dmol'
else:
method = 'matplotlib'
method = method.lower()
alist = atoms
for atoms in alist:
if not isinstance(atoms, Atomic):
raise TypeError('atoms must be an Atomic instance')
if n_modes and n_modes != len(alist):
raise RuntimeError('the number of proteins ({0}) does not match that of the modes ({1}).'
.format(len(alist), n_modes))
if '3dmol' in method:
mol = view3D(*alist, **kwargs)
return mol
else:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
cf = plt.gcf()
show = None
for child in cf.get_children():
if isinstance(child, Axes3D):
show = child
break
if show is None:
show = Axes3D(cf)
from matplotlib import colors
cnames = dict(colors.cnames)
wcolor = kwargs.get('water', 'red').lower()
avoid = np.array(colors.hex2color(cnames.pop(wcolor, cnames.pop('red'))))
for cn, val in cnames.copy().items(): # PY3K: OK
clr = np.array(colors.hex2color(val))
if clr.sum() > 2.4:
cnames.pop(cn)
elif np.abs(avoid - clr).sum() <= 0.6:
cnames.pop(cn)
cnames = list(cnames)
import random
random.shuffle(cnames)
cnames_copy = list(cnames)
min_ = list()
max_ = list()
for i, atoms in enumerate(alist):
if isinstance(atoms, AtomGroup):
title = atoms.getTitle()
else:
title = atoms.getAtomGroup().getTitle()
calpha = atoms.select('calpha')
if calpha:
partition = False
mode = modes[i] if n_modes else None
if mode is not None:
is3d = False
try:
arr = mode.getArray()
is3d = mode.is3d()
n_nodes = mode.numAtoms()
except AttributeError:
arr = mode
is3d = len(arr) == len(calpha)*3
n_nodes = len(arr)//3 if is3d else len(arr)
if n_nodes != len(calpha):
raise RuntimeError('size mismatch between the protein ({0} residues) and the mode ({1} nodes).'
.format(len(calpha), n_nodes))
partition = not is3d
if partition:
xyz = calpha._getCoords()
chids = calpha.getChids()
rbody = []
last_sign = np.sign(arr[0])
last_chid = chids[0]
rcolor = ['red', 'red', 'blue']
n = 1
for i,a in enumerate(arr):
s = np.sign(a)
ch = chids[i]
if s == 0: s = last_sign
if last_sign != s or i == len(arr)-1 or last_chid != ch:
if last_chid == ch:
rbody.append(i)
show.plot(xyz[rbody, 0], xyz[rbody, 1], xyz[rbody, 2],
label=title + '_regid%d'%n,
color=rcolor[int(last_sign+1)],
lw=kwargs.get('lw', 4))
rbody = []
n += 1
last_sign = s
last_chid = ch
rbody.append(i)
else:
for ch in HierView(calpha, chain=True):
xyz = ch._getCoords()
chid = ch.getChid()
if len(cnames) == 0:
cnames = list(cnames_copy)
show.plot(xyz[:, 0], xyz[:, 1], xyz[:, 2],
label=title + '_' + chid,
color=kwargs.get(chid, cnames.pop()).lower(),
lw=kwargs.get('lw', 4))
if mode is not None:
from prody.utilities.drawtools import drawArrow3D
XYZ = calpha._getCoords()
arr = arr.reshape((n_nodes, 3))
XYZ2 = XYZ + arr * scale
for i, xyz in enumerate(XYZ):
xyz2 = XYZ2[i]
mutation_scale = kwargs.pop('mutation_scale', 10)
drawArrow3D(xyz, xyz2, mutation_scale=mutation_scale, **kwargs)
water = atoms.select('water and noh')
if water:
xyz = atoms.select('water')._getCoords()
show.plot(xyz[:, 0], xyz[:, 1], xyz[:, 2], label=title + '_water',
color=wcolor,
ls='None', marker=kwargs.get('wmarker', '.'),
ms=kwargs.get('wsize', 6))
hetero = atoms.select('not protein and not nucleic and not water')
if hetero:
for res in HierView(hetero).iterResidues():
xyz = res._getCoords()
resname = res.getResname()
resnum = str(res.getResnum())
chid = res.getChid()
if len(cnames) == 0:
cnames = list(cnames_copy)
show.plot(xyz[:, 0], xyz[:, 1], xyz[:, 2], ls='None',
color=kwargs.get(resname, cnames.pop()).lower(),
label=title + '_' + chid + '_' + resname + resnum,
marker=kwargs.get('hmarker', 'o'),
ms=kwargs.get('hsize', 6))
xyz = atoms._getCoords()
min_.append(xyz.min(0))
max_.append(xyz.max(0))
show.set_xlabel('x')
show.set_ylabel('y')
show.set_zlabel('z')
min_ = np.array(min_).min(0)
max_ = np.array(max_).max(0)
center = (max_ + min_) / 2
half = (max_ - min_).max() / 2
show.set_xlim3d(center[0]-half, center[0]+half)
show.set_ylim3d(center[1]-half, center[1]+half)
show.set_zlim3d(center[2]-half, center[2]+half)
if kwargs.get('legend', False):
show.legend(prop={'size': 10})
if SETTINGS['auto_show']:
showFigure()
return show
def showDomains(domains, linespec='-', **kwargs):
"""A convenient function that can be used to visualize Hi-C structural domains.
*kwargs* will be passed to :func:`matplotlib.pyplot.plot`.
:arg domains: a 2D array of Hi-C domains, such as [[start1, end1], [start2, end2], ...].
:type domains: :class:`numpy.ndarray`
"""
fill_ends = kwargs.pop('fill_ends', 'close')
domains = np.array(domains)
shape = domains.shape
if len(shape) == 1:
# convert to domain list if labels are provided
indicators = np.diff(domains)
length = len(domains)
if fill_ends in ['open', 'close']:
indicators = np.append(1., indicators)
indicators[-1] = 1
elif fill_ends == 'skip':
indicators = np.append(0., indicators)
else:
raise ValueError('invalid fill_ends mode: %s'%str(fill_ends))
sites = np.where(indicators != 0)[0]
starts = sites[:-1]
ends = sites[1:]
domains = np.array([starts, ends]).T
consecutive = True
elif len(shape) == 2:
if domains.dtype == bool:
length = domains.shape[1]
domains_ = []
for h in domains:
start = None
for i, b in enumerate(h):
if b:
if start is None: # start
start = i
else:
if start is not None: # end
domains_.append([start, i-1])
start = None
if start is not None:
domains_.append([start, i])
domains = np.array(domains_)
else:
length = domains.max()
consecutive = False
else:
raise ValueError('domains must be either one or two dimensions')
from matplotlib.pyplot import figure, plot
a = []; b = []
lwd = kwargs.pop('linewidth', 1)
lwd = kwargs.pop('lw', lwd)
linewidth = np.abs(lwd)
if fill_ends == 'open' and len(domains) == 1:
domains = []
for i in range(len(domains)):
domain = domains[i]
start = domain[0]; end = domain[1]
if fill_ends == 'open' and start == 0:
a.extend([end, end])
b.extend([start, end])
elif fill_ends == 'open' and end == length-1:
a.extend([start, end])
b.extend([start, start])
else:
a.extend([start, end, end])
b.extend([start, start, end])
if not consecutive:
a.append(np.nan)
b.append(np.nan)
if lwd > 0:
x = a; y = b
else:
x = b; y = a
plt = plot(x, y, linespec, linewidth=linewidth, **kwargs)
if SETTINGS['auto_show']:
showFigure()
return plt
def showProjection(ensemble, modes, *args, **kwargs):
"""Show a projection of conformational deviations onto up to three normal
modes from the same model.
:arg ensemble: an ensemble, trajectory or a conformation for which
deviation(s) will be projected, or a deformation vector
:type ensemble: :class:`.Ensemble`, :class:`.Conformation`,
:class:`.Vector`, :class:`.Trajectory`
:arg modes: up to three normal modes
:type modes: :class:`.Mode`, :class:`.ModeSet`, :class:`.NMA`
:arg color: a color name or a list of color name, default is ``'blue'``
:type color: str, list
:arg label: label or a list of labels
:type label: str, list
:arg marker: a marker or a list of markers, default is ``'o'``
:type marker: str, list
:arg linestyle: line style, default is ``'None'``
:type linestyle: str
:arg text: list of text labels, one for each conformation
:type text: list
:arg fontsize: font size for text labels
:type fontsize: int
The projected values are by default converted to RMSD. Pass ``rmsd=False``
to use projection itself.
Matplotlib function used for plotting depends on the number of modes:
* 1 mode: :func:`~matplotlib.pyplot.hist`
* 2 modes: :func:`~matplotlib.pyplot.plot`
* 3 modes: :meth:`~mpl_toolkits.mplot3d.Axes3D.plot`"""
import matplotlib.pyplot as plt
projection = calcProjection(ensemble, modes, kwargs.pop('rmsd', True))
if projection.ndim == 1 or projection.shape[1] == 1:
show = plt.hist(projection.flatten(), *args, **kwargs)
plt.xlabel('{0} coordinate'.format(str(modes)))
plt.ylabel('Number of conformations')
return show
elif projection.shape[1] > 3:
raise ValueError('Projection onto up to 3 modes can be shown. '
'You have given {0} mode.'.format(len(modes)))
num = projection.shape[0]
markers = kwargs.pop('marker', 'o')
if isinstance(markers, str) or markers is None:
markers = [markers] * num
elif isinstance(markers, list):
if len(markers) != num:
raise ValueError('length of marker must be {0}'.format(num))
else:
raise TypeError('marker must be a string or a list')
colors = kwargs.pop('color', 'blue')
if isinstance(colors, str) or colors is None:
colors = [colors] * num
elif isinstance(colors, list):
if len(colors) != num:
raise ValueError('length of color must be {0}'.format(num))
else:
raise TypeError('color must be a string or a list')
labels = kwargs.pop('label', None)
if isinstance(labels, str) or labels is None:
labels = [labels] * num
elif isinstance(labels, list):
if len(labels) != num:
raise ValueError('length of label must be {0}'.format(num))
elif labels is not None:
raise TypeError('label must be a string or a list')
kwargs['ls'] = kwargs.pop('linestyle', None) or kwargs.pop('ls', 'None')
texts = kwargs.pop('text', None)
if texts:
if not isinstance(texts, list):
raise TypeError('text must be a list')
elif len(texts) != num:
raise TypeError('length of text must be {0}'.format(num))
size = kwargs.pop('fontsize', None) or kwargs.pop('size', None)
modes = [m for m in modes]
if len(modes) == 2:
plot = plt.plot
show = plt.gcf()
text = plt.text
else:
from mpl_toolkits.mplot3d import Axes3D
cf = plt.gcf()
show = None
for child in cf.get_children():
if isinstance(child, Axes3D):
show = child
break
if show is None:
show = Axes3D(cf)
plot = show.plot
text = show.text
indict = defaultdict(list)
for i, opts in enumerate(zip(markers, colors, labels)): # PY3K: OK
indict[opts].append(i)
args = list(args)
for opts, indices in indict.items(): # PY3K: OK
marker, color, label = opts
kwargs['marker'] = marker
kwargs['color'] = color
if label:
kwargs['label'] = label
else:
kwargs.pop('label', None)
plot(*(list(projection[indices].T) + args), **kwargs)
if texts:
kwargs = {}
if size:
kwargs['size'] = size
for args in zip(*(list(projection.T) + [texts])):
text(*args, **kwargs)
if len(modes) == 2:
plt.xlabel('{0} coordinate'.format(int(modes[0])+1))
plt.ylabel('{0} coordinate'.format(int(modes[1])+1))
elif len(modes) == 3:
show.set_xlabel('Mode {0} coordinate'.format(int(modes[0])+1))
show.set_ylabel('Mode {0} coordinate'.format(int(modes[1])+1))
show.set_zlabel('Mode {0} coordinate'.format(int(modes[2])+1))
if SETTINGS['auto_show']:
showFigure()
return show
def showEmbedding(modes, labels=None, trace=True, headtail=True, cmap='prism'):
"""Visualizes Laplacian embedding of Hi-C data.
:arg modes: modes in which loci are embedded. It can only have 2 or 3 modes for the purpose
of visualization.
:type modes: :class:`ModeSet`
:arg labels: a list of integers indicating the segmentation of the sequence.
:type labels: list
:arg trace: if **True** then the trace of the sequence will be indicated by a grey dashed line.
:type trace: bool
:arg headtail: if **True** then a star and a closed circle will indicate the head and the tail
of the sequence respectively.
:type headtail: bool
:arg cmap: the color map used to render the *labels*.
:type cmap: str
"""
V, mask = _getEigvecs(modes, True)
m,n = V.shape
if labels is not None:
if len(labels) != m:
raise ValueError('Modes (%d) and the Hi-C map (%d) should have the same number'
' of atoms. Turn off "masked" if you intended to apply the'
' modes to the full map.'
%(m, len(labels)))
if n > 3:
raise ValueError('This function can only visualize the embedding of 2 or 3 modes.')
from matplotlib.pyplot import figure, plot, scatter
from mpl_toolkits.mplot3d import Axes3D
if n == 2:
la = importLA()
X, Y = V[:,:2].T
R = np.array(range(len(X)))
R = R / la.norm(R)
X *= R; Y *= R
f = figure()
if trace:
plot(X, Y, ':', color=[0.3, 0.3, 0.3])
if labels is None:
C = 'b'
else:
C = labels
scatter(X, Y, s=30, c=C, cmap=cmap)
if headtail:
plot(X[:1], Y[:1], 'k*', markersize=12)
plot(X[-1:], Y[-1:], 'ko', markersize=12)
elif n == 3:
X, Y, Z = V[:,:3].T
f = figure()
ax = Axes3D(f)
if trace:
ax.plot(X, Y, Z, ':', color=[0.3, 0.3, 0.3])
if labels is None:
C = 'b'
else:
C = labels
ax.scatter(X, Y, Z, s=30, c=C, depthshade=True, cmap=cmap)
if headtail:
ax.plot(X[:1], Y[:1], Z[:1], 'k*', markersize=12)
ax.plot(X[-1:], Y[-1:], Z[-1:], 'ko', markersize=12)
if SETTINGS['auto_show']:
showFigure()
return f
def showProtein(*atoms, **kwargs):
"""Show protein representation using :meth:`~mpl_toolkits.mplot3d.Axes3D`.
This function is designed for generating a quick view of the contents of a
:class:`~.AtomGroup` or :class:`~.Selection`.
Protein atoms matching ``"calpha"`` selection are displayed using solid
lines by picking a random and unique color per chain. Line with can
be adjusted using *lw* argument, e.g. ``lw=12``. Default width is 4.
Chain colors can be overwritten using chain identifier as in ``A='green'``.
Water molecule oxygen atoms are represented by red colored circles. Color
can be changed using *water* keyword argument, e.g. ``water='aqua'``.
Water marker and size can be changed using *wmarker* and *wsize* keywords,
defaults values are ``wmarker='.', wsize=6``.
Hetero atoms matching ``"hetero and noh"`` selection are represented by
circles and unique colors are picked at random on a per residue basis.
Colors can be customized using residue name as in ``NAH='purple'``. Note
that this will color all distinct residues with the same name in the same
color. Hetero atom marker and size can be changed using *hmarker* and
*hsize* keywords, default values are ``hmarker='o', hsize=6``.
ProDy will set the size of axis so the representation is not distorted when
the shape of figure window is close to a square. Colors are picked at
random, except for water oxygens which will always be colored red.
*** Interactive 3D Rendering in Jupyter Notebook ***
If py3Dmol has been imported then it will be used instead to display
an interactive viewer. See :func:`view3D`
"""
from prody.dynamics.mode import Mode
method = kwargs.pop('draw', None)
modes = kwargs.get('mode', None)
scale = kwargs.get('scale', 100)
# modes need to be specifically a list or a tuple (cannot be an array)
if modes is None:
n_modes = 0
else:
modes = wrapModes(modes)
n_modes = len(modes)
if method is None:
import sys
if 'py3Dmol' in sys.modules:
method = 'py3Dmol'
else:
method = 'matplotlib'
method = method.lower()
alist = atoms
for atoms in alist:
if not isinstance(atoms, Atomic):
raise TypeError('atoms must be an Atomic instance')
if n_modes and n_modes != len(alist):
raise RuntimeError(
'the number of proteins ({0}) does not match that of the modes ({1}).'
.format(len(alist), n_modes))
if '3dmol' in method:
mol = view3D(*alist, **kwargs)
return mol
else:
kwargs.pop('mode', None)
kwargs.pop('scale', 100)
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
cf = plt.gcf()
show = None
for child in cf.get_children():
if isinstance(child, Axes3D):
show = child
break
if show is None:
show = Axes3D(cf)
from matplotlib import colors
cnames = dict(colors.cnames)
wcolor = kwargs.get('water', 'red').lower()
avoid = np.array(
colors.hex2color(cnames.pop(wcolor, cnames.pop('red'))))
for cn, val in cnames.copy().items(): # PY3K: OK
clr = np.array(colors.hex2color(val))
if clr.sum() > 2.4:
cnames.pop(cn)
elif np.abs(avoid - clr).sum() <= 0.6:
cnames.pop(cn)
cnames = list(cnames)
import random
random.shuffle(cnames)
cnames_copy = list(cnames)
min_ = list()
max_ = list()
for i, atoms in enumerate(alist):
if isinstance(atoms, AtomGroup):
title = atoms.getTitle()
else:
title = atoms.getAtomGroup().getTitle()
calpha = atoms.select('calpha')
if calpha:
partition = False
mode = modes[i] if n_modes else None
if mode is not None:
is3d = False
try:
arr = mode.getArray()
is3d = mode.is3d()
n_nodes = mode.numAtoms()
except AttributeError:
arr = mode
is3d = len(arr) == len(calpha) * 3
n_nodes = len(arr) // 3 if is3d else len(arr)
if n_nodes != len(calpha):
raise RuntimeError(
'size mismatch between the protein ({0} residues) and the mode ({1} nodes).'
.format(len(calpha), n_nodes))
partition = not is3d
if partition:
xyz = calpha._getCoords()
chids = calpha.getChids()
rbody = []
last_sign = np.sign(arr[0])
last_chid = chids[0]
rcolor = ['red', 'red', 'blue']
n = 1
for i, a in enumerate(arr):
s = np.sign(a)
ch = chids[i]
if s == 0: s = last_sign
if last_sign != s or i == len(
arr) - 1 or last_chid != ch:
if last_chid == ch:
rbody.append(i)
show.plot(xyz[rbody, 0],
xyz[rbody, 1],
xyz[rbody, 2],
label=title + '_regid%d' % n,
color=rcolor[int(last_sign + 1)],
lw=kwargs.get('lw', 4))
rbody = []
n += 1
last_sign = s
last_chid = ch
rbody.append(i)
else:
for ch in HierView(calpha, chain=True):
xyz = ch._getCoords()
chid = ch.getChid()
if len(cnames) == 0:
cnames = list(cnames_copy)
show.plot(xyz[:, 0],
xyz[:, 1],
xyz[:, 2],
label=title + '_' + chid,
color=kwargs.get(chid, cnames.pop()).lower(),
lw=kwargs.get('lw', 4))
if mode is not None:
from prody.utilities.drawtools import drawArrow3D
XYZ = calpha._getCoords()
arr = arr.reshape((n_nodes, 3))
XYZ2 = XYZ + arr * scale
for i, xyz in enumerate(XYZ):
xyz2 = XYZ2[i]
mutation_scale = kwargs.pop('mutation_scale', 10)
drawArrow3D(xyz,
xyz2,
mutation_scale=mutation_scale,
**kwargs)
water = atoms.select('water and noh')
if water:
xyz = atoms.select('water')._getCoords()
show.plot(xyz[:, 0],
xyz[:, 1],
xyz[:, 2],
label=title + '_water',
color=wcolor,
ls='None',
marker=kwargs.get('wmarker', '.'),
ms=kwargs.get('wsize', 6))
hetero = atoms.select(
'not protein and not nucleic and not water and not dummy')
if hetero:
for res in HierView(hetero).iterResidues():
xyz = res._getCoords()
resname = res.getResname()
resnum = str(res.getResnum())
chid = res.getChid()
if len(cnames) == 0:
cnames = list(cnames_copy)
show.plot(xyz[:, 0],
xyz[:, 1],
xyz[:, 2],
ls='None',
color=kwargs.get(resname, cnames.pop()).lower(),
label=title + '_' + chid + '_' + resname +
resnum,
marker=kwargs.get('hmarker', 'o'),
ms=kwargs.get('hsize', 6))
xyz = atoms._getCoords()
min_.append(xyz.min(0))
max_.append(xyz.max(0))
show.set_xlabel('x')
show.set_ylabel('y')
show.set_zlabel('z')
min_ = np.array(min_).min(0)
max_ = np.array(max_).max(0)
center = (max_ + min_) / 2
half = (max_ - min_).max() / 2
show.set_xlim3d(center[0] - half, center[0] + half)
show.set_ylim3d(center[1] - half, center[1] + half)
show.set_zlim3d(center[2] - half, center[2] + half)
if kwargs.get('legend', False):
show.legend(prop={'size': 10})
if SETTINGS['auto_show']:
showFigure()
return show
def showEmbedding(modes, labels=None, trace=True, headtail=True, cmap='prism'):
"""Visualizes Laplacian embedding of Hi-C data.
:arg modes: modes in which loci are embedded. It can only have 2 or 3 modes for the purpose
of visualization.
:type modes: :class:`ModeSet`
:arg labels: a list of integers indicating the segmentation of the sequence.
:type labels: list
:arg trace: if **True** then the trace of the sequence will be indicated by a grey dashed line.
:type trace: bool
:arg headtail: if **True** then a star and a closed circle will indicate the head and the tail
of the sequence respectively.
:type headtail: bool
:arg cmap: the color map used to render the *labels*.
:type cmap: str
"""
V, _ = _getEigvecs(modes, True)
m, n = V.shape
if labels is not None:
if len(labels) != m:
raise ValueError(
'Modes (%d) and the Hi-C map (%d) should have the same number'
' of atoms. Turn off "masked" if you intended to apply the'
' modes to the full map.' % (m, len(labels)))
if n > 3:
raise ValueError(
'This function can only visualize the embedding of 2 or 3 modes.')
from matplotlib.pyplot import figure, plot, scatter
from mpl_toolkits.mplot3d import Axes3D
if n == 2:
la = importLA()
X, Y = V[:, :2].T
R = np.array(range(len(X)))
R = R / la.norm(R)
X *= R
Y *= R
f = figure()
if trace:
plot(X, Y, ':', color=[0.3, 0.3, 0.3])
if labels is None:
C = 'b'
else:
C = labels
scatter(X, Y, s=30, c=C, cmap=cmap)
if headtail:
plot(X[:1], Y[:1], 'k*', markersize=12)
plot(X[-1:], Y[-1:], 'ko', markersize=12)
elif n == 3:
X, Y, Z = V[:, :3].T
f = figure()
ax = Axes3D(f)
if trace:
ax.plot(X, Y, Z, ':', color=[0.3, 0.3, 0.3])
if labels is None:
C = 'b'
else:
C = labels
ax.scatter(X, Y, Z, s=30, c=C, depthshade=True, cmap=cmap)
if headtail:
ax.plot(X[:1], Y[:1], Z[:1], 'k*', markersize=12)
ax.plot(X[-1:], Y[-1:], Z[-1:], 'ko', markersize=12)
if SETTINGS['auto_show']:
showFigure()
return f
def showCrossProjection(ensemble, mode_x, mode_y, scale=None, *args, **kwargs):
"""Show a projection of conformational deviations onto modes from
different models using :func:`~matplotlib.pyplot.plot`. This function
differs from :func:`.showProjection` by accepting modes from two different
models.
:arg ensemble: an ensemble or a conformation for which deviation(s) will be
projected, or a deformation vector
:type ensemble: :class:`.Ensemble`, :class:`.Conformation`,
:class:`.Vector`, :class:`.Trajectory`
:arg mode_x: projection onto this mode will be shown along x-axis
:type mode_x: :class:`.Mode`, :class:`.Vector`
:arg mode_y: projection onto this mode will be shown along y-axis
:type mode_y: :class:`.Mode`, :class:`.Vector`
:arg scale: scale width of the projection onto mode ``x`` or ``y``,
best scaling factor will be calculated and printed on the console,
absolute value of scalar makes the with of two projection same,
sign of scalar makes the projections yield a positive correlation
:type scale: str
:arg scalar: scalar factor for projection onto selected mode
:type scalar: float
:arg color: a color name or a list of color name, default is ``'blue'``
:type color: str, list
:arg label: label or a list of labels
:type label: str, list
:arg marker: a marker or a list of markers, default is ``'o'``
:type marker: str, list
:arg linestyle: line style, default is ``'None'``
:type linestyle: str
:arg text: list of text labels, one for each conformation
:type text: list
:arg fontsize: font size for text labels
:type fontsize: int
The projected values are by default converted to RMSD. Pass ``rmsd=False``
to calculate raw projection values. See :ref:`pca-xray-plotting` for a
more elaborate example."""
import matplotlib.pyplot as plt
xcoords, ycoords = calcCrossProjection(ensemble,
mode_x,
mode_y,
scale=scale,
**kwargs)
num = len(xcoords)
markers = kwargs.pop('marker', 'o')
if isinstance(markers, str) or markers is None:
markers = [markers] * num
elif isinstance(markers, list):
if len(markers) != num:
raise ValueError('length of marker must be {0}'.format(num))
else:
raise TypeError('marker must be a string or a list')
colors = kwargs.pop('color', 'blue')
if isinstance(colors, str) or colors is None:
colors = [colors] * num
elif isinstance(colors, list):
if len(colors) != num:
raise ValueError('length of color must be {0}'.format(num))
else:
raise TypeError('color must be a string or a list')
labels = kwargs.pop('label', None)
if isinstance(labels, str) or labels is None:
labels = [labels] * num
elif isinstance(labels, list):
if len(labels) != num:
raise ValueError('length of label must be {0}'.format(num))
elif labels is not None:
raise TypeError('label must be a string or a list')
kwargs['ls'] = kwargs.pop('linestyle', None) or kwargs.pop('ls', 'None')
texts = kwargs.pop('text', None)
if texts:
if not isinstance(texts, list):
raise TypeError('text must be a list')
elif len(texts) != num:
raise TypeError('length of text must be {0}'.format(num))
size = kwargs.pop('fontsize', None) or kwargs.pop('size', None)
indict = defaultdict(list)
for i, opts in enumerate(zip(markers, colors, labels)): # PY3K: OK
indict[opts].append(i)
for opts, indices in indict.items(): # PY3K: OK
marker, color, label = opts
kwargs['marker'] = marker
kwargs['color'] = color
if label:
kwargs['label'] = label
else:
kwargs.pop('label', None)
show = plt.plot(xcoords[indices], ycoords[indices], *args, **kwargs)
if texts:
kwargs = {}
if size:
kwargs['size'] = size
for x, y, t in zip(xcoords, ycoords, texts):
plt.text(x, y, t, **kwargs)
plt.xlabel('{0} coordinate'.format(mode_x))
plt.ylabel('{0} coordinate'.format(mode_y))
if SETTINGS['auto_show']:
showFigure()
return show
def showProtein(*atoms, **kwargs):
"""Show protein representation using :meth:`~mpl_toolkits.mplot3d.Axes3D`.
This function is designed for generating a quick view of the contents of a
:class:`~.AtomGroup` or :class:`~.Selection`.
Protein atoms matching ``"calpha"`` selection are displayed using solid
lines by picking a random and unique color per chain. Line with can
be adjusted using *lw* argument, e.g. ``lw=12``. Default width is 4.
Chain colors can be overwritten using chain identifier as in ``A='green'``.
Water molecule oxygen atoms are represented by red colored circles. Color
can be changed using *water* keyword argument, e.g. ``water='aqua'``.
Water marker and size can be changed using *wmarker* and *wsize* keywords,
defaults values are ``wmarker='.', wsize=6``.
Hetero atoms matching ``"hetero and noh"`` selection are represented by
circles and unique colors are picked at random on a per residue basis.
Colors can be customized using residue name as in ``NAH='purple'``. Note
that this will color all distinct residues with the same name in the same
color. Hetero atom marker and size can be changed using *hmarker* and
*hsize* keywords, default values are ``hmarker='o', hsize=6``.
ProDy will set the size of axis so the representation is not distorted when
the shape of figure window is close to a square. Colors are picked at
random, except for water oxygens which will always be colored red."""
alist = atoms
for atoms in alist:
if not isinstance(atoms, Atomic):
raise TypeError('atoms must be an Atomic instance')
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
cf = plt.gcf()
show = None
for child in cf.get_children():
if isinstance(child, Axes3D):
show = child
break
if show is None:
show = Axes3D(cf)
from matplotlib import colors
cnames = dict(colors.cnames)
wcolor = kwargs.get('water', 'red').lower()
avoid = np.array(colors.hex2color(cnames.pop(wcolor, cnames.pop('red'))))
for cn, val in cnames.items(): # PY3K: OK
clr = np.array(colors.hex2color(val))
if clr.sum() > 2.4:
cnames.pop(cn)
elif np.abs(avoid - clr).sum() <= 0.6:
cnames.pop(cn)
cnames = list(cnames)
import random
random.shuffle(cnames)
min_ = list()
max_ = list()
for atoms in alist:
if isinstance(atoms, AtomGroup):
title = atoms.getTitle()
else:
title = atoms.getAtomGroup().getTitle()
calpha = atoms.select('calpha')
if calpha:
for ch in HierView(calpha, chain=True):
xyz = ch._getCoords()
chid = ch.getChid()
show.plot(xyz[:, 0],
xyz[:, 1],
xyz[:, 2],
label=title + '_' + chid,
color=kwargs.get(chid, cnames.pop()).lower(),
lw=kwargs.get('lw', 4))
water = atoms.select('water and noh')
if water:
xyz = atoms.select('water')._getCoords()
show.plot(xyz[:, 0],
xyz[:, 1],
xyz[:, 2],
label=title + '_water',
color=wcolor,
ls='None',
marker=kwargs.get('wmarker', '.'),
ms=kwargs.get('wsize', 6))
hetero = atoms.select('not protein and not nucleic and not water')
if hetero:
for res in HierView(hetero).iterResidues():
xyz = res._getCoords()
resname = res.getResname()
resnum = str(res.getResnum())
chid = res.getChid()
show.plot(xyz[:, 0],
xyz[:, 1],
xyz[:, 2],
ls='None',
color=kwargs.get(resname, cnames.pop()).lower(),
label=title + '_' + chid + '_' + resname + resnum,
marker=kwargs.get('hmarker', 'o'),
ms=kwargs.get('hsize', 6))
xyz = atoms._getCoords()
min_.append(xyz.min(0))
max_.append(xyz.max(0))
show.set_xlabel('x')
show.set_ylabel('y')
show.set_zlabel('z')
min_ = np.array(min_).min(0)
max_ = np.array(max_).max(0)
center = (max_ + min_) / 2
half = (max_ - min_).max() / 2
show.set_xlim3d(center[0] - half, center[0] + half)
show.set_ylim3d(center[1] - half, center[1] + half)
show.set_zlim3d(center[2] - half, center[2] + half)
if kwargs.get('legend', False):
show.legend(prop={'size': 10})
if SETTINGS['auto_show']:
showFigure()
return show
def showProjection(ensemble, modes, *args, **kwargs):
"""Show a projection of conformational deviations onto up to three normal
modes from the same model.
:arg ensemble: an ensemble, trajectory or a conformation for which
deviation(s) will be projected, or a deformation vector
:type ensemble: :class:`.Ensemble`, :class:`.Conformation`,
:class:`.Vector`, :class:`.Trajectory`
:arg modes: up to three normal modes
:type modes: :class:`.Mode`, :class:`.ModeSet`, :class:`.NMA`
:arg color: a color name or a list of color name, default is ``'blue'``
:type color: str, list
:arg label: label or a list of labels
:type label: str, list
:arg marker: a marker or a list of markers, default is ``'o'``
:type marker: str, list
:arg linestyle: line style, default is ``'None'``
:type linestyle: str
:arg text: list of text labels, one for each conformation
:type text: list
:arg fontsize: font size for text labels
:type fontsize: int
The projected values are by default converted to RMSD. Pass ``rmsd=False``
to use projection itself.
Matplotlib function used for plotting depends on the number of modes:
* 1 mode: :func:`~matplotlib.pyplot.hist`
* 2 modes: :func:`~matplotlib.pyplot.plot`
* 3 modes: :meth:`~mpl_toolkits.mplot3d.Axes3D.plot`"""
import matplotlib.pyplot as plt
projection = calcProjection(ensemble, modes, kwargs.pop('rmsd', True))
if projection.ndim == 1 or projection.shape[1] == 1:
show = plt.hist(projection.flatten(), *args, **kwargs)
plt.xlabel('{0} coordinate'.format(str(modes)))
plt.ylabel('Number of conformations')
return show
elif projection.shape[1] > 3:
raise ValueError('Projection onto up to 3 modes can be shown. '
'You have given {0} mode.'.format(len(modes)))
num = projection.shape[0]
markers = kwargs.pop('marker', 'o')
if isinstance(markers, str) or markers is None:
markers = [markers] * num
elif isinstance(markers, list):
if len(markers) != num:
raise ValueError('length of marker must be {0}'.format(num))
else:
raise TypeError('marker must be a string or a list')
colors = kwargs.pop('color', 'blue')
if isinstance(colors, str) or colors is None:
colors = [colors] * num
elif isinstance(colors, list):
if len(colors) != num:
raise ValueError('length of color must be {0}'.format(num))
else:
raise TypeError('color must be a string or a list')
labels = kwargs.pop('label', None)
if isinstance(labels, str) or labels is None:
labels = [labels] * num
elif isinstance(labels, list):
if len(labels) != num:
raise ValueError('length of label must be {0}'.format(num))
elif labels is not None:
raise TypeError('label must be a string or a list')
kwargs['ls'] = kwargs.pop('linestyle', None) or kwargs.pop('ls', 'None')
texts = kwargs.pop('text', None)
if texts:
if not isinstance(texts, list):
raise TypeError('text must be a list')
elif len(texts) != num:
raise TypeError('length of text must be {0}'.format(num))
size = kwargs.pop('fontsize', None) or kwargs.pop('size', None)
modes = [m for m in modes]
if len(modes) == 2:
plot = plt.plot
show = plt.gcf()
text = plt.text
else:
from mpl_toolkits.mplot3d import Axes3D
cf = plt.gcf()
show = None
for child in cf.get_children():
if isinstance(child, Axes3D):
show = child
break
if show is None:
show = Axes3D(cf)
plot = show.plot
text = show.text
indict = defaultdict(list)
for i, opts in enumerate(zip(markers, colors, labels)): # PY3K: OK
indict[opts].append(i)
args = list(args)
for opts, indices in indict.items(): # PY3K: OK
marker, color, label = opts
kwargs['marker'] = marker
kwargs['color'] = color
if label:
kwargs['label'] = label
else:
kwargs.pop('label', None)
plot(*(list(projection[indices].T) + args), **kwargs)
if texts:
kwargs = {}
if size:
kwargs['size'] = size
for args in zip(*(list(projection.T) + [texts])):
text(*args, **kwargs)
if len(modes) == 2:
plt.xlabel('{0} coordinate'.format(int(modes[0]) + 1))
plt.ylabel('{0} coordinate'.format(int(modes[1]) + 1))
elif len(modes) == 3:
show.set_xlabel('Mode {0} coordinate'.format(int(modes[0]) + 1))
show.set_ylabel('Mode {0} coordinate'.format(int(modes[1]) + 1))
show.set_zlabel('Mode {0} coordinate'.format(int(modes[2]) + 1))
if SETTINGS['auto_show']:
showFigure()
return show
def showEllipsoid(modes, onto=None, n_std=2, scale=1., *args, **kwargs):
"""Show an ellipsoid using :meth:`~mpl_toolkits.mplot3d.Axes3D
.plot_wireframe`.
Ellipsoid volume gives an analytical view of the conformational space that
given modes describe.
:arg modes: 3 modes for which ellipsoid will be drawn.
:type modes: :class:`.ModeSet`, :class:`.PCA`, :class:`.ANM`, :class:`.NMA`
:arg onto: 3 modes onto which ellipsoid will be projected.
:type modes: :class:`.ModeSet`, :class:`.PCA`, :class:`.ANM`, :class:`.NMA`
:arg n_std: Number of standard deviations to scale the ellipsoid.
:type n_std: float
:arg scale: Used for scaling the volume of ellipsoid. This can be
obtained from :func:`.sampleModes`.
:type scale: float"""
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
if not isinstance(modes, (NMA, ModeSet)):
raise TypeError('modes must be a NMA or ModeSet instance, '
'not {0}'.format(type(modes)))
if not modes.is3d():
raise ValueError('modes must be from a 3-dimensional model')
if len(modes) != 3:
raise ValueError('length of modes is not equal to 3')
if onto is not None:
if not isinstance(onto, (NMA, ModeSet)):
raise TypeError('onto must be a NMA or ModeSet instance, '
'not {0}'.format(type(onto)))
if not onto.is3d():
raise ValueError('onto must be from a 3-dimensional model')
if len(onto) != 3:
raise ValueError('length of onto is not equal to 3')
if onto.numAtoms() != modes.numAtoms():
raise ValueError('modes and onto must have same number of atoms')
u = np.linspace(0, 2 * np.pi, 100)
v = np.linspace(0, np.pi, 100)
var = modes.getVariances()
#randn = np.random.standard_normal((1000, 3))
#coef = ((randn ** 2 * var).sum(1) ** 0.5).mean()
#scale=float(n_std)*modes.numAtoms()**.5 * float(rmsd) / coef * var **.5
scale = float(n_std) * scale * var ** 0.5
#scale=float(n_std)*modes.numAtoms()**.5*float(rmsd)/var.sum()**.5*var**.5
x = scale[0] * np.outer(np.cos(u), np.sin(v))
y = scale[1] * np.outer(np.sin(u), np.sin(v))
z = scale[2] * np.outer(np.ones(np.size(u)), np.cos(v))
if onto is not None:
change_of_basis = np.dot(modes._getArray().T, onto._getArray())
xyz = np.array([x.flatten(), y.flatten(), z.flatten()])
xyz = np.dot(xyz.T, change_of_basis)
x = xyz[:,0].reshape((100,100))
y = xyz[:,1].reshape((100,100))
z = xyz[:,2].reshape((100,100))
cf = plt.gcf()
show = None
for child in cf.get_children():
if isinstance(child, Axes3D):
show = child
break
if show is None:
show = Axes3D(cf)
show.plot_wireframe(x, y, z, rstride=6, cstride=6, *args, **kwargs)
if onto is not None:
onto = list(onto)
show.set_xlabel('Mode {0} coordinate'.format(int(onto[0])+1))
show.set_ylabel('Mode {0} coordinate'.format(int(onto[1])+1))
show.set_zlabel('Mode {0} coordinate'.format(int(onto[2])+1))
else:
modes = list(modes)
show.set_xlabel('Mode {0} coordinate'.format(int(modes[0])+1))
show.set_ylabel('Mode {0} coordinate'.format(int(modes[1])+1))
show.set_zlabel('Mode {0} coordinate'.format(int(modes[2])+1))
if SETTINGS['auto_show']:
showFigure()
return show
def showCrossProjection(ensemble, mode_x, mode_y, scale=None, *args, **kwargs):
"""Show a projection of conformational deviations onto modes from
different models using :func:`~matplotlib.pyplot.plot`. This function
differs from :func:`.showProjection` by accepting modes from two different
models.
:arg ensemble: an ensemble or a conformation for which deviation(s) will be
projected, or a deformation vector
:type ensemble: :class:`.Ensemble`, :class:`.Conformation`,
:class:`.Vector`, :class:`.Trajectory`
:arg mode_x: projection onto this mode will be shown along x-axis
:type mode_x: :class:`.Mode`, :class:`.Vector`
:arg mode_y: projection onto this mode will be shown along y-axis
:type mode_y: :class:`.Mode`, :class:`.Vector`
:arg scale: scale width of the projection onto mode ``x`` or ``y``,
best scaling factor will be calculated and printed on the console,
absolute value of scalar makes the with of two projection same,
sign of scalar makes the projections yield a positive correlation
:type scale: str
:arg scalar: scalar factor for projection onto selected mode
:type scalar: float
:arg color: a color name or a list of color name, default is ``'blue'``
:type color: str, list
:arg label: label or a list of labels
:type label: str, list
:arg marker: a marker or a list of markers, default is ``'o'``
:type marker: str, list
:arg linestyle: line style, default is ``'None'``
:type linestyle: str
:arg text: list of text labels, one for each conformation
:type text: list
:arg fontsize: font size for text labels
:type fontsize: int
The projected values are by default converted to RMSD. Pass ``rmsd=False``
to calculate raw projection values. See :ref:`pca-xray-plotting` for a
more elaborate example."""
import matplotlib.pyplot as plt
xcoords, ycoords = calcCrossProjection(ensemble, mode_x, mode_y,
scale=scale, **kwargs)
num = len(xcoords)
markers = kwargs.pop('marker', 'o')
if isinstance(markers, str) or markers is None:
markers = [markers] * num
elif isinstance(markers, list):
if len(markers) != num:
raise ValueError('length of marker must be {0}'.format(num))
else:
raise TypeError('marker must be a string or a list')
colors = kwargs.pop('color', 'blue')
if isinstance(colors, str) or colors is None:
colors = [colors] * num
elif isinstance(colors, list):
if len(colors) != num:
raise ValueError('length of color must be {0}'.format(num))
else:
raise TypeError('color must be a string or a list')
labels = kwargs.pop('label', None)
if isinstance(labels, str) or labels is None:
labels = [labels] * num
elif isinstance(labels, list):
if len(labels) != num:
raise ValueError('length of label must be {0}'.format(num))
elif labels is not None:
raise TypeError('label must be a string or a list')
kwargs['ls'] = kwargs.pop('linestyle', None) or kwargs.pop('ls', 'None')
texts = kwargs.pop('text', None)
if texts:
if not isinstance(texts, list):
raise TypeError('text must be a list')
elif len(texts) != num:
raise TypeError('length of text must be {0}'.format(num))
size = kwargs.pop('fontsize', None) or kwargs.pop('size', None)
indict = defaultdict(list)
for i, opts in enumerate(zip(markers, colors, labels)): # PY3K: OK
indict[opts].append(i)
for opts, indices in indict.items(): # PY3K: OK
marker, color, label = opts
kwargs['marker'] = marker
kwargs['color'] = color
if label:
kwargs['label'] = label
else:
kwargs.pop('label', None)
show = plt.plot(xcoords[indices], ycoords[indices], *args, **kwargs)
if texts:
kwargs = {}
if size:
kwargs['size'] = size
for x, y, t in zip(xcoords, ycoords, texts):
plt.text(x, y, t, **kwargs)
plt.xlabel('{0} coordinate'.format(mode_x))
plt.ylabel('{0} coordinate'.format(mode_y))
if SETTINGS['auto_show']:
showFigure()
return show