Exemplo n.º 1
0
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)
Exemplo n.º 2
0
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)
Exemplo n.º 3
0
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)
Exemplo n.º 4
0
    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),
Exemplo n.º 5
0
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
Exemplo n.º 6
0
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)