def test_style_alpha_beta_gamma():
x = np.linspace(0, 1, 2)
y = 2 * x
with sciplot.style(['alpha', 'beta', 'gamma'],
locale_setting='en_US.UTF-8'):
plt.plot(x, y)
return plt.gcf()
def test_plot_1():
with sciplot.style(locale_setting='en_US.UTF-8'):
x_m = 2 # scale
alpha_lst = [1, 2, 3, 4] # shape parameters
x = np.linspace(0, 6, 1000)
pdf = np.array([pareto.pdf(x, scale=x_m, b=a) for a in alpha_lst])
sciplot.set_size_cm(7)
fig, ax = plt.subplots(1, 1)
fig.suptitle(
r'Pareto PDF' +
r' $p(x \,|\, x_\mathrm{m}, \alpha) = \frac{\alpha x_\mathrm{m}^\alpha}{x^{\alpha+1}}$'
+ r' with $x_\mathrm{m}=2$')
line_plot = ax.plot(x, pdf.T)
label_lst = []
for alpha in alpha_lst:
label_lst.append(r'$\alpha=' + str(alpha) + '$')
sciplot.set_legend(ax=ax,
plot_tpl=line_plot,
label_tpl=tuple(label_lst),
loc='upper right')
ax.set_xlabel('$x$')
ax.set_ylabel(r'$p(x \,|\, x_\mathrm{m}, \alpha)$')
return fig
def test_style_fonts_cm_sans_serif():
x = np.linspace(0, 1, 2)
y = 2 * x
with sciplot.style(theme='fonts_cm_sans_serif',
locale_setting='en_US.UTF-8'):
plt.plot(x, y)
return plt.gcf()
def test_style_no_latex_serif():
x = np.linspace(0, 1, 2)
y = 2 * x
with sciplot.style(theme=['no-latex', 'serif'],
locale_setting='en_US.UTF-8'):
plt.plot(x, y)
return plt.gcf()
def test_style_clean_sans_serif():
x = np.linspace(0, 1, 2)
y = 2 * x
with sciplot.style(theme=['clean', 'sans-serif'],
locale_setting='en_US.UTF-8'):
plt.plot(x, y)
return plt.gcf()
def test_style_locale_incorrect():
local = 'Undefined_local'
x = np.linspace(0, 1, 2)
y = 2 * x
with pytest.raises(locale.Error):
with sciplot.style(locale_setting=local):
plt.plot(x, y)
return plt.gcf()
def test_plot_2():
with sciplot.style(locale_setting='en_US.UTF-8'):
np.random.seed(42)
n = 10000
mean_ar = np.array([4.5, 6.1, 8.3])
std_ar = np.array([0.2, 0.9, 0.5])
data_ar = np.array([
np.random.normal(mean_ar[0], std_ar[0], n),
np.random.normal(mean_ar[1], std_ar[1], n),
np.random.normal(mean_ar[2], std_ar[2], n)
])
sciplot.set_size_cm(16, 8)
fig, ax = plt.subplots(1, 1)
fig.suptitle('Histogram of normally distributed velocities with ' +
str(n) + ' samples')
plot_lst = []
color_lst = sciplot.get_color_lst(len(data_ar),
seaborn_color_map='rocket',
colorful=False)
for i, data in enumerate(data_ar):
ax.hist(data,
density=True,
bins=100,
alpha=0.7,
color=color_lst[i])
plot_lst.append(
Rectangle((0, 0), 1, 1, color=color_lst[i], alpha=0.7))
label_lst = []
for i in range(len(data_ar)):
label_lst.append(r'$\mu=' + str(mean_ar[i]) + r'$, $\sigma=' +
str(std_ar[i]) + r'$')
sciplot.set_legend(ax=ax,
plot_tpl=tuple(plot_lst),
label_tpl=tuple(label_lst),
loc='lower right',
outside_plot=True)
ax.set_xlabel('Velocity (m/s)')
ax.set_ylabel(r'Relative frequency')
return fig
def test_style_empty():
x = np.linspace(0, 1, 2)
y = 2 * x
with sciplot.style(locale_setting='en_US.UTF-8'):
plt.plot(x, y)
return plt.gcf()
def test_style_colors_dark():
x = np.linspace(0, 1, 2)
y = 2 * x
with sciplot.style(theme='colors_dark', locale_setting='en_US.UTF-8'):
plt.plot(x, y)
return plt.gcf()
def test_style_typesetting():
x = np.linspace(0, 1, 2)
y = 2 * x
with sciplot.style(theme='typesetting', locale_setting='en_US.UTF-8'):
plt.plot(x, y)
return plt.gcf()
