def test_get_labels(self):
one = {
'M': 1,
'X': 2,
'Y': 3,
'Area': 15.0,
'Energy': -56.9,
'Label': "one"
}
two = {
'M': 1,
'X': 2,
'Y': 3,
'Area': 15.0,
'Energy': -56.9,
'Label': "two"
}
three = {
'M': 1,
'X': 2,
'Y': 3,
'Area': 15.0,
'Energy': -56.9,
'Label': "three"
}
data = [one, two, three]
ticks = np.array([1, 2, 3])
labels = ut.get_labels(ticks, data)
expected = ['one', 'two', 'three']
assert labels == expected
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(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_get_labels(self):
H20 = data.DataSet(cation=24,
x=48,
y=2,
area=60.22,
energy=-570.00,
label="One",
nspecies=1)
H2O_2 = data.DataSet(cation=24,
x=48,
y=4,
area=60.22,
energy=-570.00,
label="Two",
nspecies=1)
dataset = [H20, H2O_2]
ticks = np.array([1, 2])
labels = ut.get_labels(ticks, dataset)
expected = ['One', 'Two']
assert labels == expected