statistics = univariate_statistics(data)
x_min = statistics[2]
x_max = statistics[3]
y_min = statistics[4]
y_max = statistics[5]
# G. Fit a quadratic polynomial to the data using quadratic_fit function
quadratic_coefficients = quadratic_fit(data)
# H. Pass 'vinet' to fit_eos
volumes = data[:, 0]
energies = data[:, 1]
vinet = fit_eos(volumes, energies, quadratic_coefficients, 'vinet')
# I. Write a convert_units module for atomic units
for i in range(len(data)):
volume = data[i][0]
energy = data[i][1]
volume = convert_units(volume, 'Cubic Bohr/atom', 'Cubic Angstrom/atom')
energy = convert_units(energy, 'Rydberg/atom', 'electron volts/atom')
# data[i][0] = volume
# data[i][1] = energy
for i in range(len(vinet)):
bulk = vinet[i]
bulk = convert_units(bulk, 'Rydberg/Cubic Bohr', 'Gigapascals')
# 4.
statistics = us.univariate_statistics(data1)
print('Statistics of data:')
print(statistics)
# 5.
quadratic_coefficients = qf.quadratic_fit(data1)
print('Quadratic coefficients of revised data:')
print(quadratic_coefficients)
# 6.
volumes = data1[:, 0]
energies = data1[:, 1]
eos_passed_info, eos_parameters = eos.fit_eos(volumes,
energies,
quadratic_coefficients,
my_eos,
number_of_points=50)
print('Fitted Data/Quadratic Coefficients:')
print(eos_passed_info)
equilibrium_volume = eos_parameters[3]
bulk_modulus = eos_parameters[1]
print(bulk_modulus)
# 7.
def convert_units(value_to_convert_from, units_of_value_converted_from,
unit_to_convert_to):
if units_of_value_converted_from == 'cubic bohr/atom':
value_in_requested_units = value_to_convert_from * 0.148185
elif units_of_value_converted_from == 'rydberg/atom':
#fit_curve = fit_curve_array(quadratic_coefficients, min_x, max_x, number_of_points=100)
#scatter_plot, curve_plot = plot_data_with_fit(array, fit_curve, data_format="bo", #fit_format="k")
#plt.show()
#plt.savefig("Initial_plot.png")
"""
"""
\/\/attempting the annotation \/\/
"""
undo_array = zip(*array)
array_2 = list(undo_array)
# fit_eos_curve, bulk_modulus = fit_eos(array_2[0], array_2[1], quadratic_coefficients, eos='murnaghan', number_of_points=50) #6
fit_eos_curve, fit_parameters = fit_eos(array[0],
array[1],
quadratic_coefficients,
eos='murnaghan',
number_of_points=50) #6
bulk_modulus = fit_parameters[1]
equilibrium_volume = fit_parameters[3]
#print(fit_eos, bulk_modulus)
def annotate_graph(symbol, structure):
ax.annotate(symbol, xy=(130, 0.001))
ax.annotate(r'$ {}\overline{{{}}} {}$'.format(structure[0:2], structure[3],
structure[1]),
xy=(115, 0))
chemical_symbol = names[0]
crystal_symetery = names[1]
d_f_c_a = names[2]
return chemical_symbol, crystal_symetery, d_f_c_a
array = two_column_text_read("Ag.Fm-3m.GGA-PBE.volumes_energies.dat")
stats = bivariate_statistics(array)
#print(stats)
quadratic_coefficients = quadratic_fit(array)
volumes = array[0, :]
energies = array[1, :]
fit_eos_array, fit_parameters = fit_eos(volumes, energies, quadratic_coefficients, eos="murnaghan", number_of_points=50)
fit_volumes = np.linspace(volumes[0], volumes[-1], 50)
bulk_modulus = fit_parameters[1]
equilibrium_volume = fit_parameters[3]
print(equilibrium_volume)
# Define an array of fit volumes going from volumes[0] to volumes[-1] but equal in number of points to fit_eos_array
plt.plot(volumes, energies, 'bo')
plt.plot(fit_volumes, fit_eos_array, 'k')
plt.xlabel(r'$\mathit{V}(\AA^3$/atom)')
plt.ylabel(r'$\mathit{E}$ (eV/atom)')
plt.title('Murnaghan Equation of State for Ag in DFT GGA-PBE')
plt.text(100.79, -287.32865, 'Created by Cheo Reyes 2020-05-12')
plt.text(120.94, -287.32250,'$\mathit{V}=120.549(\AA^3$/atom)')
plt.text(100.73, -287.31470, "Ag")
plt.text(115.19, -287.32454,'$\mathit{Fm}3\mathit{m}$')
#4
statistics = univariate_statistics(data)
#5
quadratic_coefficients = quadratic_fit(data)
#6
""" Must reformat data before finding fit curve. """
un_data = zip(*data)
data_2 = list(un_data)
volumes = data_2[0]
energies = data_2[1]
eos_fit_curve, bulk_modulus = fit_eos(volumes,
energies,
quadratic_coefficients,
eos=my_eos)
#7
""" Convert units module """
def convert_units(value_to_be_converted, units_of_value_to_be_converted_from,
units_to_be_converted_to):
"""
:param value_to_be_converted: Somevalue
:param units_of_the_value_to_be_converted_from: Accepted units: cb/a, rb/a, rb/cb
:param units_to_be_converted_to: Accepted units: ca/a, ev/a, GPa
:return: value_in_the_requested_units
"""
if units_of_value_to_be_converted_from == "cb/a":
statistics = univariate_statistics(data)
mean = (statistics[0])
standard_deviation = statistics[1]
min_x = statistics[2]
max_x = statistics[3]
min_y = statistics[4]
max_y = statistics[5]
quadratic_coefficients = quadratic_fit(data)
un_data = zip(*data)
data_list = list(un_data)
Eos_fit = fit_eos(data[0],
data[1],
quadratic_coefficients,
eos='birch-murnaghan',
number_of_points=50)
fit_curve = fit_curve_array(Eos_fit, min_x, max_x)
bulkmodulus = 230
volume = convert_units(data[0], 'cb/a')
energy = convert_units(data[1], 'rb/a')
def annotate_plot(chem_symb, crystal_symm, density_ex):
import matplotlib.pyplot as mp
annotation_chem = mp.annotate(chem_symb, xy=(200, 0.028))
annotation_crys = mp.annotate(crystal_symm, xy=(230, -.036))
annotation_dens = mp.annotate(density_ex, xy=(220, -.039))
return (annotation_chem, annotation_crys, annotation_dens)