def calculate(stoich,
data,
SE,
adsorbant,
thermochem,
max_t=1000,
min_p=-13,
max_p=5.5,
coverage=None,
transform=True):
'''Collects input variables and intitialises the calculation.
Parameters
----------
stoich : :py:class:`surfinpy.data.DataSet`
information about the stoichiometric surface
data : :py:attr:`list`
list of :py:class:`surfinpy.data.DataSet` objects on the "adsorbed" surfaces
SE : :py:attr:`float`
surface energy of the stoichiomteric surface
adsorbant : :py:attr:`float`
dft energy of adsorbing species
coverage : :py:attr:`array_like` (default None)
Numpy array containing the different coverages of adsorbant.
thermochem : :py:attr:`array_like`
Numpy array containing thermochemcial data downloaded from NIST_JANAF
for the adsorbing species.
max_t : :py:attr:`int`
Maximum temperature in the phase diagram
min_p : :py:attr:`int`
Minimum pressure of phase diagram
max_p : :py:attr:`int`
Maximum pressure of phase diagram
Returns
-------
system : :py:class:`surfinpy.plotting.PTPlot`
plotting object
'''
if coverage is None:
coverage = ut.calculate_coverage(data)
lnP, logP, T, adsorbant_t = inititalise(thermochem, adsorbant, max_t,
min_p, max_p)
nsurfaces = len(data) + 1
AE = adsorption_energy(data, stoich, adsorbant_t)
SE_array, SEABS = calculate_surface_energy(AE, lnP, T, coverage, SE,
nsurfaces)
ticks = np.unique([SE_array])
if transform is True:
SE_array = ut.transform_numbers(SE_array, ticks)
phase_grid = np.reshape(SE_array, (lnP.size, T.size))
SEABS = np.reshape(SEABS, (lnP.size, T.size))
data.insert(0, stoich)
y = logP
x = T
z = phase_grid
system = plotting.PTPlot(x, y, z)
return system
def calculate(data, bulk, deltaX, deltaY, x_energy, y_energy, mu_z, exp_x,
exp_y):
"""Initialise the free energy calculation.
Parameters
----------
data : :py:attr:`list`
List containing the :py:class:`surfinpy.data.DataSet` objects for each phase
bulk : :py:class:`surfinpy.data.ReferenceDataSet`
Reference dataset
x : :py:attr:`dict`
X axis chemical potential values
y : :py:attr:`dict`
Y axis chemical potential values
nphases : :py:attr:`int`
Number of phases
x_energy : :py:attr:`float`
DFT 0K energy for species x
y_energy : :py:attr:`float`
DFT 0K energy for species y
mu_z : :py:attr:`float`
Set chemical potential for species y
exp_x : :py:attr:`float`
Experimental correction for species x
exp_y : :py:attr:`float`
Experimental correction for species y
Returns
-------
system : :py:class:`surfinpy.plotting.MuTPlot`
Plotting object
"""
nphases = len(data)
X = np.arange(deltaX['Range'][0], deltaX['Range'][1], 0.01, dtype="float")
Y = np.arange(deltaY['Range'][0], deltaY['Range'][1], 0.01, dtype="float")
vd.recalculate_vib(data, bulk)
phases, SE = evaluate_phases(
data,
bulk,
X,
Y,
nphases,
x_energy,
y_energy,
mu_z,
exp_x,
exp_y,
)
ticks = np.unique([phases])
colors = ut.list_colors(data, ticks)
phases = ut.transform_numbers(phases, ticks)
Z = np.reshape(phases, (Y.size, X.size))
SE = np.reshape(SE, (Y.size, X.size))
labels = ut.get_labels(ticks, data)
system = plotting.MuTPlot(X, Y, Z, labels, ticks, colors, deltaX['Label'],
deltaY['Label'])
return system
def calculate(stoich,
data,
SE,
adsorbant,
thermochem,
max_t=1000,
min_p=-13,
max_p=5.5,
coverage=None):
'''Collects input variables and intitialises the calculation.
Parameters
----------
stoich : dictionary
information about the stoichiometric surface
data : list
list of dictionaries containing information on the "adsorbed" surfaces
SE : float
surface energy of the stoichiomteric surface
adsorbant : float
dft energy of adsorbing species
coverage : array like (default None)
Numpy array containing the different coverages of adsorbant.
thermochem : array like
Numpy array containing thermochemcial data downloaded from NIST_JANAF
for the adsorbing species.
max_t : int
Maximum temperature in the phase diagram
min_p : int
Minimum pressure of phase diagram
max_p : int
Maximum pressure of phase diagram
Returns
-------
system : class object
plotting object
'''
if coverage is None:
coverage = ut.calculate_coverage(data)
lnP, logP, T, adsorbant_t = inititalise(thermochem, adsorbant, max_t,
min_p, max_p)
nsurfaces = len(data) + 1
AE = adsorption_energy(data, stoich, adsorbant_t)
SE_array, SE = calculate_surface_energy(AE, lnP, T, coverage, SE,
nsurfaces)
ticks = np.unique([SE_array])
SE_array = ut.transform_numbers(SE_array, ticks)
phase_grid = np.reshape(SE_array, (lnP.size, T.size))
data.insert(0, stoich)
y = logP
x = T
z = phase_grid
system = pvt_plot.PVTPlot(x, y, z)
return system, SE
def calculate(data, bulk, deltaX, deltaY, x_energy=0, y_energy=0,
temperature=0, output="Phase_Diagram.png"):
"""Initialise the surface energy calculation.
Parameters
----------
data : list
List of dictionaries for each phase
bulk : dictionary
Dictionary containing data for bulk
deltaX : dictionary
Range of chemical potential/label for species X
DeltaY : dictionary
Range of chemical potential/label for species Y
x_energy : float
DFT energy of adsorbing species
y_energy : float
DFT energy of adsorbing species
temperature : int
Temperature
output : str
Output file name
Returns
-------
system : class obj
Plotting object
"""
data = sorted(data, key=lambda k: (k['Y']))
nsurfaces = len(data)
X = np.arange(deltaX['Range'][0], deltaX['Range'][1],
0.025, dtype="float")
Y = np.arange(deltaY['Range'][0], deltaY['Range'][1],
0.025, dtype="float")
X = X - x_energy
Y = Y - y_energy
phases = evaluate_phases(data, bulk, X, Y,
nsurfaces, x_energy, y_energy)
ticks = np.unique([phases])
phases = ut.transform_numbers(phases, ticks)
Z = np.reshape(phases, (Y.size, X.size))
labels = ut.get_labels(ticks, data)
system = chemical_potential_plot.ChemicalPotentialPlot(X,
Y,
Z,
labels,
ticks,
deltaX['Label'],
deltaY['Label'])
return system
def calculate(data, bulk, deltaX, deltaY, x_energy=0, y_energy=0, increments=0.025):
"""Initialise the surface energy calculation.
Parameters
----------
data : :py:attr:`list`
List of :py:class:`surfinpy.data.DataSet` for each phase
bulk : :py:class:`surfinpy.data.ReferenceDataSet`
Data for bulk
deltaX : :py:attr:`dict`
Range of chemical potential/label for species X
DeltaY : :py:attr:`dict`
Range of chemical potential/label for species Y
x_energy : :py:attr:`float`
DFT energy of adsorbing species
y_energy : :py:attr:`float`
DFT energy of adsorbing species
Returns
-------
system : :py:class:`surfinpy.plotting.ChemicalPotentialPlot`
Plotting object
"""
nsurfaces = len(data)
X = np.arange(deltaX['Range'][0], deltaX['Range'][1],
increments, dtype="float")
Y = np.arange(deltaY['Range'][0], deltaY['Range'][1],
increments, dtype="float")
X = X - x_energy
Y = Y - y_energy
phases, SE = evaluate_phases(data, bulk, X, Y,
nsurfaces, x_energy, y_energy)
ticks = np.unique([phases])
colors = ut.list_colors(data, ticks)
phases = ut.transform_numbers(phases, ticks)
Z = np.reshape(phases, (Y.size, X.size))
SE = np.reshape(SE, (Y.size, X.size))
labels = ut.get_labels(ticks, data)
system = plotting.ChemicalPotentialPlot(X,
Y,
Z,
labels,
ticks,
colors,
deltaX['Label'],
deltaY['Label'])
return system, SE
def test_transform_numbers(self):
Z = np.array([2, 2, 3, 3])
ticks = np.array([2, 3])
Z = ut.transform_numbers(Z, ticks)
expected = np.array([0, 0, 1, 1])
assert np.array_equal(Z, expected)