To use this demo you to to have a jupyter server available and able to receive connections
from localhost
. To start a local server, use the following command:
jupyter lab --NotebookApp.token=
--NotebookApp.allow_origin='*' --no-browser
A simple widgets example.
# borrowed from https://jupyterlite.readthedocs.io/en/latest/_static/lab/index.html from ipywidgets import interact, IntSlider from IPython.display import Markdown, display slider = IntSlider() @interact(cookies=slider) def cookies(cookies=slider.value, calories=(0, 150)): total_calories = calories * cookies if cookies: display( Markdown( f"If each cookie contains _{calories} calories_, \ _{cookies} cookies_ contain **{total_calories} calories**!" ) ) else: display(Markdown(f"No cookies!")) if total_calories > 2000: display(Markdown(f"> Maybe that's too many cookies..."))
import numpy as np import matplotlib.pyplot as plt %matplotlib inline from ipywidgets import interact, interactive from IPython.display import clear_output, display, HTML import numpy as np from scipy import integrate from matplotlib import pyplot as plt from mpl_toolkits.mplot3d import Axes3D from matplotlib.colors import cnames from matplotlib import animation def solve_lorenz( N=10, angle=0.0, max_time=4.0, sigma=10.0, beta=8./3, rho=28.0): fig = plt.figure() ax = fig.add_axes([0, 0, 1, 1], projection='3d') ax.axis('off') # prepare the axes limits ax.set_xlim((-25, 25)) ax.set_ylim((-35, 35)) ax.set_zlim((5, 55)) def lorenz_deriv(x_y_z, t0, sigma=sigma, beta=beta, rho=rho): """Compute the time-derivative of a Lorenz system.""" x, y, z = x_y_z return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z] # Choose random starting points, uniformly distributed from -15 to 15 np.random.seed(1) x0 = -15 + 30 * np.random.random((N, 3)) # Solve for the trajectories t = np.linspace(0, max_time, int(250*max_time)) x_t = np.asarray([integrate.odeint(lorenz_deriv, x0i, t) for x0i in x0]) # choose a different color for each trajectory colors = plt.cm.viridis(np.linspace(0, 1, N)) for i in range(N): x, y, z = x_t[i,:,:].T lines = ax.plot(x, y, z, '-', c=colors[i]) plt.setp(lines, linewidth=2) ax.view_init(30, angle) plt.show() return t, x_t w = interactive(solve_lorenz, angle=(0.,360.), max_time=(0.1, 4.0), N=(0,50), sigma=(0.0,50.0), rho=(0.0,50.0)) display(w)