def test_find_peaks():
x = np.linspace(0,10,300)
y = 0.2*gauss(x-0.5,.1) + gauss(x-2,.1) + 0.7*gauss(x-3,0.1) + gauss(x-6,1)
# ymin=0.4: ignore first peak at x=0.5
idx0, pos0 = find_peaks(y,x, ymin=0.4)
assert idx0 == [60, 90, 179]
assert np.allclose(pos0, np.array([2,3,6.]), atol=1e-3)
def test_find_peaks():
x = np.linspace(0,10,300)
y = 0.2*gauss(x-0.5,.1) + gauss(x-2,.1) + 0.7*gauss(x-3,0.1) + gauss(x-6,1)
# ymin=0.4: ignore first peak at x=0.5
idx0, pos0 = find_peaks(y,x, ymin=0.4)
assert idx0 == [60, 90, 179]
assert np.allclose(pos0, np.array([2,3,6.]), atol=1e-3)
def test_gibbs():
# number of varied axis points
nax = 6
# phonon freq axis
freq = np.linspace(0, 1000, 300) # cm^-1
T = np.linspace(5, 2000, 50)
P = np.linspace(0, 5, 2)
# 2d case
case = '2d'
cell_a = np.linspace(2.5, 3.5, nax)
cell_c = np.linspace(3, 3.8, nax)
volfunc_ax = lambda x: x[0]**2 * x[1]
axes_flat = np.array([x for x in product(cell_a, cell_c)])
V = np.array([volfunc_ax(x) for x in axes_flat])
cell_a_mean = cell_a.mean()
cell_c_mean = cell_c.mean()
cell_a_min = cell_a.min()
cell_c_min = cell_c.min()
etot = np.array([(a - cell_a_mean)**2.0 + (c - cell_c_mean)**2.0
for a, c in axes_flat])
phdos = []
Vmax = V.max()
# phonon dos (just a gaussian) shifted to lower (higher) freqs for higher
# (lower) volume
for ii in range(axes_flat.shape[0]):
a, c = axes_flat[ii, :]
fc = 550 - 50 * V[ii] / Vmax
phdos.append(np.array([freq, gauss(freq - fc, 100) * 0.01]).T)
gibbs = Gibbs(T=T,
P=P,
etot=etot,
phdos=phdos,
axes_flat=axes_flat,
volfunc_ax=volfunc_ax,
case=case,
dosarea=None)
gibbs.set_fitfunc('C',
lambda x, y: num.Spline(x, y, s=None, k=5, eps=1e-5))
g = gibbs.calc_G(calc_all=True)
dr = 'files/gibbs/2d'
for name in os.listdir(dr):
fn = '%s/%s' % (dr, name)
gref = io.read_h5(fn)
print("testing: %s" % fn)
compare_dicts_with_arrays(gref, g)
tools.assert_dict_with_all_types_almost_equal(gref,
g,
keys=list(gref.keys()),
atol=1e-8,
rtol=1e-3)
# 1d case
case = '1d'
V = np.linspace(10, 20, nax)
axes_flat = V**(1 / 3.) # cubic
volfunc_ax = lambda x: x[0]**3.0
etot = (V - V.mean())**2
fcenter = 450 + 100 * (axes_flat - axes_flat.min())
# fake phonon dos data (Gaussian), shift to lower freq for higher volume
phdos = [np.array([freq, gauss(freq - fc, 100)]).T for fc in fcenter[::-1]]
gibbs = Gibbs(T=T,
P=P,
etot=etot,
phdos=phdos,
axes_flat=axes_flat,
volfunc_ax=volfunc_ax,
case=case,
dosarea=None)
gibbs.set_fitfunc('C',
lambda x, y: num.Spline(x, y, s=None, k=5, eps=1e-5))
g = gibbs.calc_G(calc_all=True)
dr = 'files/gibbs/1d'
for name in os.listdir(dr):
fn = '%s/%s' % (dr, name)
gref = io.read_h5(fn)
print("testing: %s" % fn)
compare_dicts_with_arrays(gref, g)
tools.assert_dict_with_all_types_almost_equal(gref,
g,
keys=list(gref.keys()),
atol=1e-14,
rtol=1e-8)
# test enthalpy stuff for 1d case
# E(V)
ev = num.PolyFit1D(g['/ax0/V'], g['/ax0/Etot'], deg=5)
# P(V)
pv = lambda v: -ev(v, der=1) * constants.eV_by_Ang3_to_GPa
assert np.allclose(g['/P/P'], pv(g['/#opt/P/V']))
assert np.allclose(g['/#opt/P/H'],
ev(g['/#opt/P/V']) + g['/P/P']*g['/#opt/P/V'] / \
constants.eV_by_Ang3_to_GPa)
axes_flat = np.array([x for x in product(cell_a, cell_c)])
V = np.array([volfunc_ax(x) for x in axes_flat])
cell_a_mean = cell_a.mean()
cell_c_mean = cell_c.mean()
cell_a_min = cell_a.min()
cell_c_min = cell_c.min()
etot = np.array([(a - cell_a_mean)**2.0 + (c - cell_c_mean)**2.0
for a, c in axes_flat])
phdos = []
Vmax = V.max()
# phonon dos (just a gaussian) shifted to lower (higher) freqs for higher
# (lower) volume
for ii in range(axes_flat.shape[0]):
a, c = axes_flat[ii, :]
fc = 550 - 50 * V[ii] / Vmax
phdos.append(np.array([freq, gauss(freq - fc, 100) * 0.01]).T)
gibbs = Gibbs(T=T,
P=P,
etot=etot,
phdos=phdos,
axes_flat=axes_flat,
volfunc_ax=volfunc_ax,
case=case,
dosarea=None)
gibbs.set_fitfunc('C',
lambda x, y: num.Spline(x, y, s=None, k=5, eps=1e-5))
g = gibbs.calc_G(calc_all=True)
common.makedirs('../files/gibbs/2d')
io.write_h5('../files/gibbs/2d/%s.h5' % gethostname(),
filt_dct(g),
volfunc_ax = lambda x: x[0]**2 * x[1]
axes_flat = np.array([x for x in product(cell_a, cell_c)])
V = np.array([volfunc_ax(x) for x in axes_flat])
cell_a_mean = cell_a.mean()
cell_c_mean = cell_c.mean()
cell_a_min = cell_a.min()
cell_c_min = cell_c.min()
etot = np.array([(a-cell_a_mean)**2.0 + (c-cell_c_mean)**2.0 for a,c in axes_flat])
phdos = []
Vmax = V.max()
# phonon dos (just a gaussian) shifted to lower (higher) freqs for higher
# (lower) volume
for ii in range(axes_flat.shape[0]):
a,c = axes_flat[ii,:]
fc = 550 - 50 * V[ii] / Vmax
phdos.append(np.array([freq,gauss(freq-fc,100)*0.01]).T)
gibbs = Gibbs(T=T, P=P, etot=etot, phdos=phdos, axes_flat=axes_flat,
volfunc_ax=volfunc_ax, case=case, dosarea=None)
gibbs.set_fitfunc('C', lambda x,y: num.Spline(x,y,s=None,k=5, eps=1e-5))
g = gibbs.calc_G(calc_all=True)
common.makedirs('../files/gibbs/2d')
io.write_h5('../files/gibbs/2d/%s.h5' %gethostname(), filt_dct(g), mode='w')
# 1d case
case = '1d'
V = np.linspace(10,20,nax)
axes_flat = V**(1/3.) # cubic
volfunc_ax = lambda x: x[0]**3.0
etot = (V-V.mean())**2
def test_gibbs():
# number of varied axis points
nax = 6
# phonon freq axis
freq = np.linspace(0,1000,300) # cm^-1
T = np.linspace(5, 2000, 50)
P = np.linspace(0,5,2)
# 2d case
case = '2d'
cell_a = np.linspace(2.5,3.5,nax)
cell_c = np.linspace(3,3.8,nax)
volfunc_ax = lambda x: x[0]**2 * x[1]
axes_flat = np.array([x for x in product(cell_a, cell_c)])
V = np.array([volfunc_ax(x) for x in axes_flat])
cell_a_mean = cell_a.mean()
cell_c_mean = cell_c.mean()
cell_a_min = cell_a.min()
cell_c_min = cell_c.min()
etot = np.array([(a-cell_a_mean)**2.0 + (c-cell_c_mean)**2.0 for a,c in axes_flat])
phdos = []
Vmax = V.max()
# phonon dos (just a gaussian) shifted to lower (higher) freqs for higher
# (lower) volume
for ii in range(axes_flat.shape[0]):
a,c = axes_flat[ii,:]
fc = 550 - 50 * V[ii] / Vmax
phdos.append(np.array([freq,gauss(freq-fc,100)*0.01]).T)
gibbs = Gibbs(T=T, P=P, etot=etot, phdos=phdos, axes_flat=axes_flat,
volfunc_ax=volfunc_ax, case=case, dosarea=None)
gibbs.set_fitfunc('C', lambda x,y: num.Spline(x,y,s=None,k=5, eps=1e-5))
g = gibbs.calc_G(calc_all=True)
dr = 'files/gibbs/2d'
for name in os.listdir(dr):
fn = '%s/%s' %(dr, name)
gref = io.read_h5(fn)
print "testing: %s" %fn
compare_dicts_with_arrays(gref, g)
tools.assert_dict_with_all_types_almost_equal(gref,
g,
keys=gref.keys(),
atol=1e-8, rtol=1e-3)
# 1d case
case = '1d'
V = np.linspace(10,20,nax)
axes_flat = V**(1/3.) # cubic
volfunc_ax = lambda x: x[0]**3.0
etot = (V-V.mean())**2
fcenter = 450 + 100*(axes_flat - axes_flat.min())
# fake phonon dos data (Gaussian), shift to lower freq for higher volume
phdos = [np.array([freq,gauss(freq-fc, 100)]).T for fc in
fcenter[::-1]]
gibbs = Gibbs(T=T, P=P, etot=etot, phdos=phdos, axes_flat=axes_flat,
volfunc_ax=volfunc_ax, case=case, dosarea=None)
gibbs.set_fitfunc('C', lambda x,y: num.Spline(x,y,s=None,k=5, eps=1e-5))
g = gibbs.calc_G(calc_all=True)
dr = 'files/gibbs/1d'
for name in os.listdir(dr):
fn = '%s/%s' %(dr, name)
gref = io.read_h5(fn)
print "testing: %s" %fn
compare_dicts_with_arrays(gref, g)
tools.assert_dict_with_all_types_almost_equal(gref,
g,
keys=gref.keys(),
atol=1e-14, rtol=1e-14)
# test enthalpy stuff for 1d case
# E(V)
ev = num.PolyFit1D(g['/ax0/V'], g['/ax0/Etot'], deg=5)
# P(V)
pv = lambda v: -ev(v, der=1)*constants.eV_by_Ang3_to_GPa
assert np.allclose(g['/P/P'], pv(g['/#opt/P/V']))
assert np.allclose(g['/#opt/P/H'],
ev(g['/#opt/P/V']) + g['/P/P']*g['/#opt/P/V'] / \
constants.eV_by_Ang3_to_GPa)