Beispiel #1
0
def gaussian_measured(resolution, inverted=True):
    x, y, dx, screen = make_lattice_2D(resolution, 5, 5)
    wall = make_border_wall_2D(resolution, 5, 5, weight=100)
    potential = wall

    psi_ = exp(-50 * (x**2 + y**2)) + 0j
    psi = psi_ * 0
    psi[4:-4, 4:-4] = psi_[4:-4, 4:-4]
    psi = normalize_2D(psi, dx)

    episode_length = 0.1

    mask = exp(-10000 * ((x - 0.2)**20 + (y + 0.3)**20))
    if inverted:
        mask = 1 - mask

    return {
        "dx": dx,
        "screen": screen,
        "psi": psi,
        "potential": potential,
        "episode_length": episode_length,
        "measurements": {
            0.02: {
                "mask": mask,
                "forced": True
            },
        },
    }
Beispiel #2
0
def slit_base(resolution):
    x, y, dx, screen = make_lattice_2D(resolution, 10, 10, extra_height=5)
    potential = exp(-(8 * (x + 1))**4) * 2000
    psi_ = exp(-10 * (x + 4)**2 - 10 * y**2 + 15j * x)
    psi = psi_ * 0
    psi[4:-4, 4:-4] = psi_[4:-4, 4:-4]
    psi = normalize_2D(psi, dx)
    episode_length = 0.5

    return x, y, dx, screen, psi, potential, episode_length
Beispiel #3
0
def convex_mirror(resolution):
    x, y, dx, screen = make_lattice_2D(resolution, 5, 4)
    potential = exp(-(x - 3.3 + 0.15 * y * y)**4) * 7000

    psi_ = exp(-30 * ((x + 1.5)**2 + y**2) + 35j * x)
    psi = psi_ * 0
    psi[4:-4, 4:-4] = psi_[4:-4, 4:-4]
    psi = normalize_2D(psi, dx)

    episode_length = 0.18

    return dx, screen, psi, potential, episode_length
Beispiel #4
0
def moving_gaussian(resolution):
    x, y, dx, screen = make_lattice_2D(resolution, 5, 2)
    potential = 0 * x

    psi_ = exp(-15 * ((x + 1.5)**2 + y**2) + 30j * x) + 0j
    psi = psi_ * 0
    psi[4:-4, 4:-4] = psi_[4:-4, 4:-4]
    psi = normalize_2D(psi, dx)

    episode_length = 0.1

    return dx, screen, psi, potential, episode_length
Beispiel #5
0
def static_gaussian(resolution):
    x, y, dx, screen = make_lattice_2D(resolution, 5, 5)
    wall = make_border_wall_2D(resolution, 5, 5, weight=100)
    potential = wall

    psi_ = exp(-15 * (x**2 + y**2)) + 0j
    psi = psi_ * 0
    psi[4:-4, 4:-4] = psi_[4:-4, 4:-4]
    psi = normalize_2D(psi, dx)

    episode_length = 0.1

    return dx, screen, psi, potential, episode_length
Beispiel #6
0
def gaussian_superposition(resolution):
    x, y, dx, screen = make_lattice_2D(resolution, 5, 5)
    potential = 0 * x

    psi_ = exp(-60 * ((x + 1.5)**2 + (y + 0.2)**2)) + exp(-5 *
                                                          ((x - 1.5)**2 +
                                                           (y - 0.2)**2)) + 0j
    psi = psi_ * 0
    psi[4:-4, 4:-4] = psi_[4:-4, 4:-4]
    psi = normalize_2D(psi, dx)

    episode_length = 0.15

    return dx, screen, psi, potential, episode_length
Beispiel #7
0
def box_with_stuff(resolution):
    x, y, dx, screen = make_lattice_2D(resolution, 10, 0.5)
    wall = make_border_wall_2D(resolution, 10, 0.5, weight=1000)
    potential = wall
    potential += exp(-(x - 0)**2 - (y - 1.8)**2) * 200
    potential += exp(-(x + y * 0.7)**8 - (x * 0.7 - y - 2)**8) * 1000

    psi_ = exp(-2 * (x + 2.5)**2 - 2 * y**2 + 5j * x)
    psi = psi_ * 0
    psi[4:-4, 4:-4] = psi_[4:-4, 4:-4]
    psi = normalize_2D(psi, dx)

    episode_length = 3.0

    return dx, screen, psi, potential, episode_length
Beispiel #8
0
def tunneling(resolution, weight=200, omega=14.75, extra_width=4):
    x, y, dx, screen = make_lattice_2D(resolution,
                                       7,
                                       extra_width=extra_width,
                                       extra_height=0)
    potential = exp(-(2 * x)**6) * weight
    potential = maximum(potential, (1 - exp(-(0.35 * y)**6)) * 1000)

    psi_ = exp(-1.1 * ((x + 2.5)**2 + y**2) + omega * 1j * x)
    psi = psi_ * 0
    psi[4:-4, 4:-4] = psi_[4:-4, 4:-4]
    psi = normalize_2D(psi, dx)

    episode_length = 0.4

    return dx, screen, psi, potential, episode_length
Beispiel #9
0
def harmonic_potential(resolution):
    x, y, dx, screen = make_lattice_2D(resolution, 5, 1)
    potential = (x * x + y * y) * 800

    psi_ = exp(-50 * ((x + 1)**2 + (y - 0.2)**2) + 15j * y)
    psi = psi_ * 0
    psi[4:-4, 4:-4] = psi_[4:-4, 4:-4]
    psi = normalize_2D(psi, dx)

    episode_length = 0.4

    return {
        "dx": dx,
        "screen": screen,
        "psi": psi,
        "potential": potential,
        "episode_length": episode_length,
        "dt": 0.001 * dx,
    }
Beispiel #10
0
def colliding_gaussians(resolution):
    x, y, dx, screen = make_lattice_2D(resolution, 5, 5)
    potential = 0 * x

    psi_ = exp(-15 * ((x + 1.5)**2 +
                      (y + 0.2)**2) + 31j * x) + exp(-17 *
                                                     ((x - 1.5)**2 +
                                                      (y - 0.2)**2) - 28j * x)
    psi = psi_ * 0
    psi[4:-4, 4:-4] = psi_[4:-4, 4:-4]
    psi = normalize_2D(psi, dx)

    episode_length = 0.15

    return {
        "dx": dx,
        "screen": screen,
        "psi": psi,
        "potential": potential,
        "episode_length": episode_length,
    }
Beispiel #11
0
                        type=float,
                        default=0.08)
    args = parser.parse_args()

    if args.folder and args.animate:
        print('Animation is not supported while dumping to disk')
        sys.exit()

    if args.folder and not args.animate and not os.path.isdir(args.folder):
        print("Target folder doesn't exist")
        sys.exit()

    width, height = RESOLUTIONS[args.resolution]

    if args.episode == 'static_gaussian':
        x, y, dx, screen = make_lattice_2D(args.resolution, 5, 5)
        potential = 0 * x
        psi = exp(-3 * (x**2 + y**2)) + 0j
        psi = normalize_2D(psi[screen], dx)
        total_samples = 16000
    elif args.episode == 'gaussian_superposition':
        dx, screen, psi, potential, episode_length = gaussian_superposition(
            args.resolution)
        i = 0
        t = 0
        dt = 0.004 * dx
        while t < episode_length:
            if i % 100 == 0:
                print("Precalculating: {} % complete".format(
                    int(100 * t / episode_length)))
            patch = advance_pde(t,