Complete examples

Basic example

import h_transport_materials as htm

import matplotlib.pyplot as plt

u = htm.ureg

D_1 = htm.Diffusivity(
   D_0=1 * u.m**2 * u.s**-1,
   E_D=0.2* u.eV * u.particle**-1,
)

D_2 = htm.Diffusivity(
   D_0=2 * u.cm**2 * u.s**-1,
   E_D=0.3* u.eV * u.particle**-1,
)

htm.plotting.plot(D_1, label="diffusivity 1")
htm.plotting.plot(D_2, label="diffusivity 2")

plt.yscale("log")
plt.legend()
plt.show()

(Source code)

Convert properties units

import h_transport_materials as htm

u = htm.ureg

my_diffusivity = htm.Diffusivity(
    D_0=1e-9 * u.m**2 * u.s**-1,
    E_D=0.5 * u.eV * u.particle**-1
)


print(my_diffusivity.pre_exp.to(u.miles**2 * u.month**-1))
# please don't do this...

print(my_diffusivity.act_energy.to(u.kcal * u.mol**-1))

1.0153714565349241e-09 mile ** 2 / month
11.530273915309515 kilocalorie / mole

Accessing the internal database: basic

import h_transport_materials as htm
import matplotlib.pyplot as plt

flinak_diffusivities = htm.diffusivities.filter(material=htm.FLINAK)

htm.plotting.plot(flinak_diffusivities)

plt.legend()
plt.yscale("log")
plt.show()

(Source code)

Accessing the internal database: colour_by

import h_transport_materials as htm
import matplotlib.pyplot as plt

tungsten_diffusivities = htm.diffusivities.filter(material=htm.TUNGSTEN)

fig, axs = plt.subplots(3, 1, sharey=True, sharex=True)

plt.sca(axs[0])
htm.plotting.plot(tungsten_diffusivities)

plt.sca(axs[1])
htm.plotting.plot(tungsten_diffusivities, colour_by="author")

plt.sca(axs[2])
htm.plotting.plot(tungsten_diffusivities, colour_by="isotope")

plt.yscale("log")
plt.show()

(Source code)

Accessing the internal database: advanced

import h_transport_materials as htm
import matplotlib.pyplot as plt

mat_to_colour = {
    htm.COPPER: "tab:orange",
    htm.CUCRZR: "tab:brown",
    htm.TUNGSTEN: "tab:grey",
}

fig, axs = plt.subplots(ncols=1, nrows=3, figsize=(6.4, 8), sharex=True)

# change the format of units in matplotlib
htm.ureg.mpl_formatter = "{:~P}"

for i, group in enumerate([htm.diffusivities, htm.solubilities, htm.permeabilities]):
    plt.sca(axs[i])
    filtered_group = group.filter(material=list(mat_to_colour.keys()))
    htm.plotting.plot(
        filtered_group, alpha=0.6, colour_by="material", key_to_colour=mat_to_colour
    )
    plt.yscale("log")
    plt.xlabel("")  # remove default xlabel

axs[0].get_legend().remove()
axs[1].get_legend().remove()

axs[0].set_title("Diffusivity")
axs[1].set_title("Solubility")
axs[2].set_title("Permeability")

plt.xlabel(f"Inverse temperature ({axs[-1].xaxis.get_units():~P})")
plt.show()

(Source code)

Accessing the internal database: statistics

import h_transport_materials as htm
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import norm


def plot_histogram(data):
    # plot histogram
    counts, bins, _ = plt.hist(data, alpha=0.7, edgecolor="tab:blue")

    # fit with Gaussian
    (mu_pre_exp, sigma_pre_exp) = norm.fit(data)
    x_axis = np.linspace(min(data), max(data))
    best_fit = norm.pdf(x_axis, mu_pre_exp, sigma_pre_exp)
    bin_width = np.diff(bins)[0]
    scaling_factor = sum(bin_width * counts)

    # plot best fit
    plt.plot(x_axis, scaling_factor * best_fit)


fig, (axs_top, axs_bot) = plt.subplots(nrows=2, ncols=2, sharey=True, figsize=(6.4, 6))

for i, group in enumerate([htm.diffusivities, htm.solubilities]):
    # filter Steel properties
    props = group.filter(material=htm.Steel)

    all_pre_exp = [np.log10(prop.pre_exp.magnitude) for prop in props]
    all_act_energy = [prop.act_energy.magnitude for prop in props]

    plt.sca(axs_top[i])
    plot_histogram(all_pre_exp)

    plt.sca(axs_bot[i])
    plot_histogram(all_act_energy)


axs_top[0].set_title("Diffusivity")
axs_top[1].set_title("Solubility")

axs_top[0].set_ylabel("Number of properties")
axs_bot[0].set_ylabel("Number of properties")

axs_top[0].set_xlabel(f"log10 ( $D_0$ {htm.diffusivities[0].units:~P} ) ")
axs_top[1].set_xlabel(f"log10 ( $S_0$ {htm.solubilities[0].units:~P} ) ")
axs_bot[0].set_xlabel(f"$E_D$ (eV)")
axs_bot[1].set_xlabel(f"$E_S$ (eV)")

axs_bot[0].set_xlim(0, 0.6)
axs_bot[1].set_xlim(0, 0.6)

plt.tight_layout()
plt.show()

(Source code)