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)