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()