def test_pdos():
filename = tools.unpack_compressed('files/pw.md.out.gz', prefix=__file__)
pp = parse.PwMDOutputFile(filename=filename)
traj = pp.get_traj()
# timestep dt
# -----------
# Only needed in pd.*_pdos(), not in pd.velocity(). Here is why:
#
# vacf_pdos, direct_pdos:
# If we compute the *normalized* VCAF, then dt is a factor:
# <v_x(0) v_x(t)> = 1/dt^2 <dx(0) dx(t)>
# which cancels in the normalization. dt is not needed in the velocity
# calculation, hence not
# V=velocity(coords, dt=dt)
# only
# V=velocity(coords).
V = traj.velocity # Ang / fs
mass = traj.mass # amu
dt = traj.timestep # fs
timeaxis = traj.timeaxis
assert np.allclose(150.0, dt * constants.fs / constants.tryd) # dt=150 Rydberg time units
fd, dd = pd.direct_pdos(V, m=mass, dt=dt, npad=1, tonext=False)
fv, dv = pd.vacf_pdos(V, m=mass, dt=dt, mirr=True)
np.testing.assert_array_almost_equal(fd, fv, err_msg="freq not equal")
np.testing.assert_array_almost_equal(dd, dv, err_msg="dos not equal")
assert np.allclose(fd, np.loadtxt('files/ref_test_pdos/fd.txt.gz'))
assert np.allclose(fv, np.loadtxt('files/ref_test_pdos/fv.txt.gz'))
assert np.allclose(dd, np.loadtxt('files/ref_test_pdos/dd.txt.gz'))
assert np.allclose(dv, np.loadtxt('files/ref_test_pdos/dv.txt.gz'))
df = fd[1] - fd[0]
print "Nyquist freq: %e" %(0.5/dt)
print "df: %e:" %df
print "timestep: %f fs = %f tryd" %(dt, dt * constants.fs / constants.tryd)
print "timestep pw.out: %f tryd" %(pp.timestep)
# API
fd, dd, ffd, fdd, si = pd.direct_pdos(V, m=mass, dt=dt, full_out=True)
fv, dv, ffv, fdv, si, vacf, fft_vacf = pd.vacf_pdos(V, m=mass, dt=dt,
mirr=True, full_out=True)
# Test padding for speed.
fd, dd, ffd, fdd, si = pd.direct_pdos(V, m=mass, dt=dt, npad=1, tonext=True, \
full_out=True)
assert len(fd) == len(dd)
assert len(ffd) == len(fdd)
# If `tonext` is used, full fft array lengths must be a power of two.
assert len(ffd) >= 2*V.shape[timeaxis] - 1
assert np.log2(len(ffd)) % 1.0 == 0.0
def test_pdos():
filename = tools.unpack_compressed('files/pw.md.out.gz', prefix=__file__)
pp = parse.PwMDOutputFile(filename=filename)
traj = pp.get_traj()
# timestep dt
# -----------
# Only needed in pd.*_pdos(), not in pd.velocity(). Here is why:
#
# vacf_pdos, direct_pdos:
# If we compute the *normalized* VCAF, then dt is a factor:
# <v_x(0) v_x(t)> = 1/dt^2 <dx(0) dx(t)>
# which cancels in the normalization. dt is not needed in the velocity
# calculation, hence not
# V=velocity(coords, dt=dt)
# only
# V=velocity(coords).
V = traj.velocity # Ang / fs
mass = traj.mass # amu
dt = traj.timestep # fs
timeaxis = traj.timeaxis
assert np.allclose(150.0, dt * constants.fs / constants.tryd) # dt=150 Rydberg time units
fd, dd = pd.direct_pdos(V, m=mass, dt=dt, npad=1, tonext=False)
fv, dv = pd.vacf_pdos(V, m=mass, dt=dt, mirr=True)
np.testing.assert_array_almost_equal(fd, fv, err_msg="freq not equal")
np.testing.assert_array_almost_equal(dd, dv, err_msg="dos not equal")
assert np.allclose(fd, np.loadtxt('files/ref_test_pdos/fd.txt.gz'))
assert np.allclose(fv, np.loadtxt('files/ref_test_pdos/fv.txt.gz'))
assert np.allclose(dd, np.loadtxt('files/ref_test_pdos/dd.txt.gz'))
assert np.allclose(dv, np.loadtxt('files/ref_test_pdos/dv.txt.gz'))
df = fd[1] - fd[0]
print("Nyquist freq: %e" %(0.5/dt))
print("df: %e:" %df)
print("timestep: %f fs = %f tryd" %(dt, dt * constants.fs / constants.tryd))
print("timestep pw.out: %f tryd" %(pp.timestep))
# API
fd, dd, ffd, fdd, si = pd.direct_pdos(V, m=mass, dt=dt, full_out=True)
fv, dv, ffv, fdv, si, vacf, fft_vacf = pd.vacf_pdos(V, m=mass, dt=dt,
mirr=True, full_out=True)
# Test padding for speed.
fd, dd, ffd, fdd, si = pd.direct_pdos(V, m=mass, dt=dt, npad=1, tonext=True, \
full_out=True)
assert len(fd) == len(dd)
assert len(ffd) == len(fdd)
# If `tonext` is used, full fft array lengths must be a power of two.
assert len(ffd) >= 2*V.shape[timeaxis] - 1
assert np.log2(len(ffd)) % 1.0 == 0.0
# `nfreq` frequencies for each x,y,z component of each atom
freqs = rand(natoms, 3, nfreq) * fmax
for i in range(natoms):
for k in range(3):
# vector w/ frequencies: freqs[i,k,:] <=> f_j, j=0, ..., nfreq-1
# sum_j sin(2*pi*f_j*t)
coords[:, i, k] = np.sin(2 * pi * freqs[i, k, :][:, None] *
taxis).sum(axis=0)
##arr = coords
arr = crys.velocity_traj(coords)
massvec = rand(natoms)
# no mass weighting
M = None
f4, y4n = pydos.vacf_pdos(arr, dt=dt, m=M, mirr=True)
f5, y5n = pydos.direct_pdos(arr, dt=dt, m=M)
# with mass weighting
M = massvec
f6, y6nm = pydos.vacf_pdos(arr, dt=dt, m=M, mirr=True)
f7, y7nm = pydos.direct_pdos(arr, dt=dt, m=M)
if use_fourier:
# For each atom, write an array (time.shape[0], 3) with coords at all time
# steps, run fourier.x on that, sum up the power spectra. No mass
# weighting.
fourier_in_data = np.zeros((arr.shape[time_axis], 7))
fourier_in_data[:, 0] = np.arange(arr.shape[time_axis])
fourier_out_fn = pj(fourier_dir, 'fourier_3d.log')
common.system("rm -f %s" % fourier_out_fn)
coords = np.empty((nstep, natoms, 3))
# `nfreq` frequencies for each x,y,z component of each atom
freqs = rand(natoms, 3, nfreq)*fmax
for i in range(natoms):
for k in range(3):
# vector w/ frequencies: freqs[i,k,:] <=> f_j, j=0, ..., nfreq-1
# sum_j sin(2*pi*f_j*t)
coords[:,i,k] = np.sin(2*pi*freqs[i,k,:][:,None]*taxis).sum(axis=0)
##arr = coords
arr = crys.velocity_traj(coords)
massvec = rand(natoms)
# no mass weighting
M = None
f4, y4n = pydos.vacf_pdos(arr, dt=dt, m=M, mirr=True)
f5, y5n = pydos.direct_pdos(arr, dt=dt, m=M)
# with mass weighting
M = massvec
f6, y6nm = pydos.vacf_pdos(arr, dt=dt, m=M, mirr=True)
f7, y7nm = pydos.direct_pdos(arr, dt=dt, m=M)
if use_fourier:
# For each atom, write an array (time.shape[0], 3) with coords at all time
# steps, run fourier.x on that, sum up the power spectra. No mass
# weighting.
fourier_in_data = np.zeros((arr.shape[time_axis],7))
fourier_in_data[:,0] = np.arange(arr.shape[time_axis])
fourier_out_fn = pj(fourier_dir, 'fourier_3d.log')
common.system("rm -f %s" %fourier_out_fn)
