def test_h5():
try:
import h5py
dct1 = \
{'/a': 'abcgs',
'/b/c/x1': 3,
'/b/c/x2': rand(2,3),
}
# writing a dct w/o leading slash will always be read back in *with*
# leading slash
dct2 = \
{'a': 'abciqo4iki',
'b/c/x1': 3,
'b/c/x2': rand(2,3),
}
for idx, dct in enumerate([dct1, dct2]):
h5fn = os.path.join(testdir, 'test_%i.h5' % idx)
io.write_h5(h5fn, dct)
read_dct = io.read_h5(h5fn)
for kk in list(read_dct.keys()):
assert kk.startswith('/')
for kk in list(dct.keys()):
key = '/' + kk if not kk.startswith('/') else kk
tools.assert_all_types_equal(dct[kk], read_dct[key])
# write mode='a', test appending
h5fn = os.path.join(testdir, 'test_append.h5')
io.write_h5(h5fn, {'/a': 1.0})
read_dct = io.read_h5(h5fn)
assert list(read_dct.keys()) == ['/a']
assert read_dct['/a'] == 1.0
# append '/b', using {'/a': 1.0, '/b': 2.0} would be an error since /a
# already exists, use mode='w' then, but this overwrites all!
io.write_h5(h5fn, {'/b': 2.0}, mode='a')
read_dct2 = io.read_h5(h5fn)
# sort(...): sort possible [/b, /a] -> [/a, /b]
assert np.sort(np.array(list(
read_dct2.keys()))).tolist() == ['/a', '/b']
assert read_dct2['/a'] == 1.0
assert read_dct2['/b'] == 2.0
# overwrite
io.write_h5(h5fn, {'/b': 22.0, '/c': 33.0}, mode='w')
read_dct3 = io.read_h5(h5fn)
assert np.sort(np.array(list(
read_dct3.keys()))).tolist() == ['/b', '/c']
except ImportError:
tools.skip("skipping test_h5, no h5py importable")
def test_h5():
try:
import h5py
dct1 = \
{'/a': 'abcgs',
'/b/c/x1': 3,
'/b/c/x2': rand(2,3),
}
# writing a dct w/o leading slash will always be read back in *with*
# leading slash
dct2 = \
{'a': 'abciqo4iki',
'b/c/x1': 3,
'b/c/x2': rand(2,3),
}
for idx,dct in enumerate([dct1, dct2]):
h5fn = os.path.join(testdir, 'test_%i.h5' %idx)
io.write_h5(h5fn, dct)
read_dct = io.read_h5(h5fn)
for kk in read_dct.keys():
assert kk.startswith('/')
for kk in dct.keys():
key = '/'+kk if not kk.startswith('/') else kk
tools.assert_all_types_equal(dct[kk], read_dct[key])
# write mode='a', test appending
h5fn = os.path.join(testdir, 'test_append.h5')
io.write_h5(h5fn, {'/a': 1.0})
read_dct = io.read_h5(h5fn)
assert read_dct.keys() == ['/a']
assert read_dct['/a'] == 1.0
# append '/b', using {'/a': 1.0, '/b': 2.0} would be an error since /a
# already exists, use mode='w' then, but this overwrites all!
io.write_h5(h5fn, {'/b': 2.0}, mode='a')
read_dct2 = io.read_h5(h5fn)
# sort(...): sort possible [/b, /a] -> [/a, /b]
assert np.sort(np.array(read_dct2.keys())).tolist() == ['/a', '/b']
assert read_dct2['/a'] == 1.0
assert read_dct2['/b'] == 2.0
# overwrite
io.write_h5(h5fn, {'/b': 22.0, '/c': 33.0}, mode='w')
read_dct3 = io.read_h5(h5fn)
assert np.sort(np.array(read_dct3.keys())).tolist() == ['/b', '/c']
except ImportError:
tools.skip("skipping test_h5, no h5py importable")
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)
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)
