def band(phonon, start, end, npts, cartesian, output, branch, output_npy):
"Given phonon data in IDF format, plot band structure along one direction"
# phonon is the path to a directory with IDF phonon data
# read phonon data
from mccomponents.sample.phonon import periodicdispersion_fromidf as pd
import mcni, numpy as np
disp = pd(phonon)
# and construct the proxy to the c++ data object
import mccomponents.homogeneous_scatterer as mh
cdisp = mh.scattererEngine(disp)
# create Q array
Qs = np.array(start) + (np.array(end) - np.array(start)) * np.arange(
0, 1, 1. / npts)[:, np.newaxis]
# Q will be cartesian
if not cartesian:
# read reciprocal basis vectors from Qgridinfo
import os
reci_basis = recibasis_fromQgridinfo(os.path.join(phonon, 'Qgridinfo'))
Qs = np.dot(Qs, reci_basis)
# compute energies
nbr = disp.dispersion.nBranches
Es = [cdisp.energy(br, mcni.vector3(*Q)) for Q in Qs for br in range(nbr)]
Es = np.array(Es)
Es.shape = -1, nbr
# output
if not output_npy:
output_npy = 'bands-start%s-end%s.npy' % (start, end)
np.save(output_npy, Es)
try:
import matplotlib as mpl
except ImportError:
import warnings
warnings.warn(
"Plotting needs matplotlib. Please install python matplotlib")
return
mpl.use("Agg")
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
if branch == -1:
for i in range(nbr):
ax.plot(Es[:, i])
else:
ax.plot(Es[:, branch])
if output:
fig.savefig(output)
else:
plt.show()
return
def band(phonon, start, end, npts, cartesian, output, branch):
"Given phonon data in IDF format, plot band structure along one direction"
# phonon is the path to a directory with IDF phonon data
# read phonon data
from mccomponents.sample.phonon import periodicdispersion_fromidf as pd
import mcni, numpy as np
disp = pd(phonon)
# and construct the proxy to the c++ data object
import mccomponents.homogeneous_scatterer as mh
cdisp = mh.scattererEngine(disp)
# create Q array
Qs = np.array(start) + (np.array(end)-np.array(start)) * np.arange(0, 1, 1./npts)[:, np.newaxis]
# Q will be cartesian
if not cartesian:
# read reciprocal basis vectors from Qgridinfo
import os
reci_basis = recibasis_fromQgridinfo(os.path.join(phonon, 'Qgridinfo'))
Qs = np.dot(Qs, reci_basis)
# compute energies
nbr = disp.dispersion.nBranches
Es = [cdisp.energy(br, mcni.vector3(*Q))
for Q in Qs
for br in range(nbr)]
Es = np.array(Es)
Es.shape = -1, nbr
# output
try:
import matplotlib as mpl
except ImportError:
import warnings
warnings.warn("Plotting needs matplotlib. Please install python matplotlib")
return
mpl.use("Agg")
import matplotlib.pyplot as plt
fig = plt.figure(); ax = fig.add_subplot(111)
if branch == -1:
for i in range(nbr):
ax.plot(Es[:, i])
else:
ax.plot(Es[:, branch])
if output:
fig.savefig(output)
else:
plt.show()
return
def slice(crystal, phonon, start, end, npts, cartesian, outhist, eaxis):
"Given phonon data in IDF format, compute slice of SQE data along a specific reciprocal space direction"
# phonon is the path to a directory with IDF phonon data
# read phonon data
from mccomponents.sample.phonon import periodicdispersion_fromidf as pd
import mcni, numpy as np
disp = pd(phonon)
# and construct a proxy to the c++ data object
import mccomponents.homogeneous_scatterer as mh
cdisp = mh.scattererEngine(disp)
# create Q array
start = np.array(start)
end = np.array(end)
step = (end-start)/npts
Qs = start + step * np.arange(0, npts, 1.)[:, np.newaxis]
# Qs: cartesian, hkls: miller indexes
import os
reci_basis = recibasis_fromQgridinfo(os.path.join(phonon, 'Qgridinfo'))
inv_reci_basis = np.linalg.inv(reci_basis)
if cartesian:
hkls = np.dot(Qs, inv_reci_basis)
# these vectors will be used later in method Qtox
start = np.dot(start, inv_reci_basis)
step = np.dot(step, inv_reci_basis)
end = np.dot(end, inv_reci_basis)
else:
hkls = Qs
Qs = np.dot(hkls, reci_basis)
# gather energies and polarizations
nQ = len(Qs)
nbr = disp.dispersion.nBranches
natoms = disp.dispersion.nAtoms
# energies
Es = [cdisp.energy(br, mcni.vector3(*Q))
for Q in Qs
for br in range(nbr)
]
Es = np.array(Es)
Es.shape = nQ, nbr
# polarizations
pols = [
np.array(cdisp.polarization(br, atom, mcni.vector3(*Q)))
for Q in Qs
for br in range(nbr)
for atom in range(natoms)
]
pols = np.array(pols)
pols.shape = nQ, nbr, natoms, 3
# get atom positions from crystal structure file
from danse.ins.matter.Parsers import getParser
parser = getParser(os.path.splitext(crystal)[-1][1:])
structure = parser.parseFile(crystal)
atom_positions = [atom.xyz for atom in structure]
# create "events" for later histogramming process to construct slice
events = computeEvents(hkls, Es, pols, atom_positions, reci_basis)
# for x axis of the histogram (along Q)
maxx = np.linalg.norm(end-start)
xaxis = 0, maxx, maxx/npts
# Eaxis = 0, np.max(Es) * 1.1, 1.
Eaxis = eaxis
# functor to convert hkl to "x" value
from distutils.version import LooseVersion
def Qtox(hkl):
if LooseVersion(np.__version__) >= LooseVersion("1.8"):
x = np.linalg.norm(hkl-start, axis=-1)
else:
x = np.array(map(np.linalg.norm, hkl-start))
mask = x==x
return x, mask
# histogramming
h = makeSlice(events, xaxis, Eaxis, Qtox)
# output
import histogram.hdf as hh
hh.dump(h, outhist)
return
def slice(crystal, phonon, start, end, npts, cartesian, outhist, eaxis):
"Given phonon data in IDF format, compute slice of SQE data along a specific reciprocal space direction"
# phonon is the path to a directory with IDF phonon data
# read phonon data
from mccomponents.sample.phonon import periodicdispersion_fromidf as pd
import mcni, numpy as np
disp = pd(phonon)
# and construct a proxy to the c++ data object
import mccomponents.homogeneous_scatterer as mh
cdisp = mh.scattererEngine(disp)
# create Q array
start = np.array(start)
end = np.array(end)
step = (end - start) / npts
Qs = start + step * np.arange(0, npts, 1.)[:, np.newaxis]
# Qs: cartesian, hkls: miller indexes
import os
reci_basis = recibasis_fromQgridinfo(os.path.join(phonon, 'Qgridinfo'))
inv_reci_basis = np.linalg.inv(reci_basis)
if cartesian:
hkls = np.dot(Qs, inv_reci_basis)
# these vectors will be used later in method Qtox
start = np.dot(start, inv_reci_basis)
step = np.dot(step, inv_reci_basis)
end = np.dot(end, inv_reci_basis)
else:
hkls = Qs
Qs = np.dot(hkls, reci_basis)
# gather energies and polarizations
nQ = len(Qs)
nbr = disp.dispersion.nBranches
natoms = disp.dispersion.nAtoms
# energies
Es = [cdisp.energy(br, mcni.vector3(*Q)) for Q in Qs for br in range(nbr)]
Es = np.array(Es)
Es.shape = nQ, nbr
# polarizations
pols = [
np.array(cdisp.polarization(br, atom, mcni.vector3(*Q))) for Q in Qs
for br in range(nbr) for atom in range(natoms)
]
pols = np.array(pols)
pols.shape = nQ, nbr, natoms, 3
# get atom positions from crystal structure file
from diffpy.Structure.Parsers import getParser
parser = getParser(os.path.splitext(crystal)[-1][1:])
structure = parser.parseFile(crystal)
atom_positions = [atom.xyz for atom in structure]
# create "events" for later histogramming process to construct slice
events = computeEvents(hkls, Es, pols, atom_positions, reci_basis)
# for x axis of the histogram (along Q)
maxx = np.linalg.norm(end - start)
xaxis = 0, maxx, maxx / npts
# Eaxis = 0, np.max(Es) * 1.1, 1.
Eaxis = eaxis
# functor to convert hkl to "x" value
from distutils.version import LooseVersion
def Qtox(hkl):
if LooseVersion(np.__version__) >= LooseVersion("1.8"):
x = np.linalg.norm(hkl - start, axis=-1)
else:
x = np.array(map(np.linalg.norm, hkl - start))
mask = x == x
return x, mask
# histogramming
h = makeSlice(events, xaxis, Eaxis, Qtox)
# output
import histogram.hdf as hh
hh.dump(h, outhist)
return
