示例#1
0
def J_video(J, n, saveFile = None):
    """Given current density J, plot quiver animation"""
    Jn = J[n]
    def update_quiver(num,Q):
        real_Jn = (Jn*np.e**(-1*1j*num/10.0)).real
        Q.set_UVC(real_Jn[:,:,0], real_Jn[:,:,1])
        return Q,

    # py.quiver(Jn[:,:,0], Jn[:,:,1])
    fig,ax = py.subplots(1,1)
    im2 = draw_circle()
    Q = ax.quiver(Jn[:,:,0], Jn[:,:,1],color='blue',scale=30000)
    anim = animation.FuncAnimation(fig, update_quiver, fargs=(Q,), interval=10, blit = False)
    if saveFile:
        ani.save(saveFile)
    py.title("Current Density")

    py.figure(2)
    x,y = np.meshgrid(np.arange(120), np.arange(120))
    U = Jn[:,:,0].real
    V = Jn[:,:,1].real
    py.streamplot(x,y,U,V)
    draw_circle()
    
    py.show()
示例#2
0
def main():
    # For timing purposes
    start = time.time()
    pr = cProfile.Profile()
    pr.enable()
    # Ex 5.21

    q1 = [0.05, 0.0]
    q2 = [-0.05, 0.0]
    a = -0.5
    b = 0.5
    num_squares = 100

    x = np.linspace(a, b, num_squares)
    xcoor, ycoor = np.meshgrid(x, x)

    phi = np.zeros((num_squares, num_squares))
    E = np.zeros((num_squares, num_squares))
    for i in range(num_squares):
        for j in range(num_squares):
            r1 = np.sqrt((xcoor[i][j] - q1[0])**2 + (ycoor[i][j] - q1[1])**2)
            r2 = np.sqrt((xcoor[i][j] - q2[0])**2 + (ycoor[i][j] - q2[1])**2)
            phi[i][j] = (1 / 4 * np.pi * cons.epsilon_0) * (1 / r1 - 1 / r2)

    plt.imshow(phi)
    plt.title("electric potential of two particles")
    plt.colorbar()
    plt.show()

    Ex, Ey = np.gradient(phi)

    # Distance formula
    def R(x, y):
        return np.sqrt(x**2 + y**2)

    # Potential for every point
    q1x, q1y = xcoor - q1[0], ycoor - q1[1]
    q2x, q2y = xcoor - q2[0], ycoor - q2[1]

    # Electric field via partial derivative
    h = 1e-5

    def V(q, x, y):
        return q * (1 / 4 * np.pi * cons.epsilon_0) * (1 / R(x, y))

    vx1 = (V(1, (q1x + h / 2), q1y) - V(1, (q1x - h / 2), q1y)) / h
    vy1 = (V(1, q1x, (q1y + h / 2)) - V(1, q1x, (q1y - h / 2))) / h
    vx2 = (V(-1, (q2x + h / 2), q2y) - V(-1, (q2x - h / 2), q2y)) / h
    vy2 = (V(-1, q2x, (q2y + h / 2)) - V(-1, q2x, (q2y - h / 2))) / h
    Vx = vx1 + vx2
    Vy = vy1 + vy2

    streamplot(xcoor, ycoor, Vx, Vy, density=2.5, linewidth=0.5, arrowsize=0.8)
    plt.title("Electric Field of a Dipole")
    plt.show()

    pr.disable()
    # pr.print_stats(sort='time')
    end = time.time()
    print(f"Program took :{end - start} seconds to run")
示例#3
0
 def Bsf(self):
     if not hasattr(self, 'Br'):
         self.Bfeild()
     pl.streamplot(self.r,
                   self.z,
                   self.Br.T,
                   self.Bz.T,
                   color=self.Br.T,
                   cmap=pl.cm.RdBu)
     pl.clim([-1.5, 1.5])
示例#4
0
def plot_streamlines(xin,yin):
    x = np.linspace(xin[0], xin[-1], 64)
    y = np.linspace(yin[0], yin[-1], 64)
    X,Y = np.meshgrid(x,y)
    relX = (X - xin[0]) / (xin[-1] - xin[0])
    relY = (Y - xin[0]) / (yin[-1] - yin[0])
    A = 2*pi
    B = pi
    U = 1 - 4 * np.cos(B*relY) * np.sin(A*relX)**2 * np.sin(B*relY)
    V = 4 * np.cos(A*relX) * np.sin(B*relY)**2 * np.sin(A*relX)
    S = np.sqrt(U*U + V*V)
    pl.streamplot(X, Y, U, V,
                  linewidth=np.sqrt(S),
                  color="lightsteelblue",
                  density=1.1,
                  minlength=0.9,
                  arrowstyle='-')
示例#5
0
def plot_B_streamlines(mi, mq, mu, imax, imin):
    '''
        Plotting B-field lines on top of Stokes I map
        '''
    import pylab as plt
    import os

    if np.shape(np.shape(mi))[0] == 1:
        return print('********** NO PLOT: Maps must be 2D arrays ************'
                     ), os.system('afplay /System/Library/Sounds/Sosumi.aiff')

    nx = np.shape(mi)[0]
    ny = np.shape(mi)[1]
    x = np.linspace(0, nx - 1, nx)
    y = np.linspace(0, ny - 1, ny)
    xp = x
    yp = y
    y2, x2 = np.meshgrid(xp, yp, indexing='ij')
    psi = -0.5 * np.arctan2(mu, mq)
    x3 = np.cos(psi)
    y3 = np.sin(psi)
    plt.figure(10)
    plt.contourf(mi,
                 levels=np.arange(imin, imax, (imax - imin) / 500.),
                 extent=[0, nx, 0, ny])
    plt.colorbar()
    plt.streamplot(x2,
                   y2,
                   x3,
                   y3,
                   density=[3, 3],
                   color='w',
                   arrowstyle='-',
                   linewidth=0.5)
    plt.show()
    return
示例#6
0
import pylab, numpy as np
import math
xvalues, yvalues = pylab.meshgrid(np.arange(-4,4, 0.1), np.arange(-4,4,0.1))

vx = yvalues*np.cos(xvalues*yvalues)
vy = xvalues*np.cos(xvalues*yvalues)

pylab.streamplot(xvalues, yvalues, vx, vy)

pylab.show()
示例#7
0
文件: velocity.py 项目: olegrog/latex
np.set_printoptions(threshold='nan')
magU = np.sqrt(U*U+V*V)
cmap = py.cm.get_cmap('coolwarm')
lmax = 3.2 # (int(magU.max()*10) + 1.)/10
lev = np.linspace(0, lmax, 11)
CF = py.contourf(x, y, magU, cmap=cmap, levels=lev)

# streamplot version (need matplotlib 1.2.1, use only evenly grid)
from matplotlib.mlab import griddata
interp = 'linear' # 'nn'
N = 50
xi = np.linspace(0, 1, 2*N)
yi = np.linspace(0, .5, 4*N)
U = griddata(X0, Y0, U0, xi, yi, interp=interp)
V = griddata(X0, Y0, V0, xi, yi, interp=interp)
py.streamplot(xi, yi, U, V, color='k', density=0.89, minlength=.2, arrowstyle='->')

py.text(-.07, .4, r'$u_{i1}$', fontsize=10)
py.xlabel(r'$x$', labelpad=-3)
py.ylabel(r'$z$', labelpad=-3, rotation=0)
py.xlim(0,1)
py.ylim(0,0.5)
ax = py.axes()
ax.set_aspect('equal')
ax.set_yticks([0,0.5])
ax.set_xticks([0,1])

from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(py.axes())
cax = divider.append_axes("right", "5%", pad="5%")
py.colorbar(CF, cax=cax)
示例#8
0
                density.T,
                100,
                norm=colors.SymLogNorm(linthresh=density.max() / 20,
                                       vmin=density_min,
                                       vmax=density_max),
                cmap='bwr')

    #    pl.colorbar()
    pl.title(r'Time = ' + "%.2f" % (time_array[start_index + file_number]) +
             " ps")

    pl.streamplot(x[:, 0],
                  y[0, :],
                  j_x_m,
                  j_y_m,
                  density=2,
                  color='k',
                  linewidth=2.5,
                  arrowsize=3,
                  transform=rot + base)
    pl.xlim([0, 5])
    pl.ylim([-1.25, 0])

    #    print (j_x)

    #radius = 0.1666
    #theta = np.arange(0., 2*np.pi, 0.01)
    #pl.plot(radius*np.cos(theta), radius*np.sin(theta), color = 'k', alpha = 0.3)

    #pl.xlim([q1[0], q1[-1]])
    #pl.ylim([q2[0], q2[-1]])
示例#9
0
def View2D(g, option=0):

    # plot and check the field
    fig = figure(num=2, figsize=(16, 6))
    # fig.suptitle('Efit Analysis', fontsize=20)

    ax = subplot2grid((3, 3), (0, 0), colspan=1, rowspan=3)
    nlev = 100
    minf = np.min(g.psi)
    maxf = np.max(g.psi)
    levels = np.arange(
        np.float(nlev)) * (maxf - minf) / np.float(nlev - 1) + minf
    ax.contour(g.r, g.z, g.psi, levels=levels)
    #    ax.set_xlim([0,6])
    ax.set_xlabel('R')
    ax.set_ylabel('Z')
    ax.yaxis.label.set_rotation('horizontal')

    ax.set_aspect('equal')

    # fig.suptitle('Efit Analysis', fontsize=20)
    # title('Efit Analysis', fontsize=20)
    text(0.5,
         1.08,
         'Efit Analysis',
         horizontalalignment='center',
         fontsize=20,
         transform=ax.transAxes)

    draw()
    if option == 0: show(block=False)

    plot(g.xlim, g.ylim, 'g-')

    draw()

    csb = contour(g.r, g.z, g.psi, levels=[g.sibdry])

    clabel(
        csb,
        [g.sibdry],  # label the level
        inline=1,
        fmt='%9.6f',
        fontsize=14)

    csb.collections[0].set_label('boundary')

    # pl1=plot(g.rbdry,g.zbdry,'b-',marker='x', label='$\psi=$'+ np.str(g.sibdry))
    #  legend(bbox_to_anchor=(0., 1.05, 1., .105), loc='upper left')

    draw()

    # fig.set_tight_layout(True)

    #  show(block=False)

    # Function fpol and qpsi are given between simagx (psi on the axis) and sibdry (
    # psi on limiter or separatrix). So the toroidal field (fpol/R) and the q profile are within these boundaries

    npsigrid = old_div(
        np.arange(np.size(g.pres)).astype(float), (np.size(g.pres) - 1))

    fpsi = np.zeros((2, np.size(g.fpol)), np.float64)
    fpsi[0, :] = (g.simagx + npsigrid * (g.sibdry - g.simagx))
    fpsi[1, :] = g.fpol

    boundary = np.array([g.xlim, g.ylim])

    rz_grid = Bunch(
        nr=g.nx,
        nz=g.ny,  # Number of grid points
        r=g.r[:, 0],
        z=g.z[0, :],  # R and Z as 1D arrays
        simagx=g.simagx,
        sibdry=g.sibdry,  # Range of psi
        psi=g.psi,  # Poloidal flux in Weber/rad on grid points
        npsigrid=npsigrid,  # Normalised psi grid for fpol, pres and qpsi
        fpol=g.fpol,  # Poloidal current function on uniform flux grid
        pres=g.pres,  # Plasma pressure in nt/m^2 on uniform flux grid
        qpsi=g.qpsi,  # q values on uniform flux grid
        nlim=g.nlim,
        rlim=g.xlim,
        zlim=g.ylim)  # Wall boundary

    critical = analyse_equil(g.psi, g.r[:, 0], g.z[0, :])

    n_opoint = critical.n_opoint
    n_xpoint = critical.n_xpoint
    primary_opt = critical.primary_opt
    inner_sep = critical.inner_sep
    opt_ri = critical.opt_ri
    opt_zi = critical.opt_zi
    opt_f = critical.opt_f
    xpt_ri = critical.xpt_ri
    xpt_zi = critical.xpt_zi
    xpt_f = critical.xpt_f

    psi_inner = 0.6
    psi_outer = 0.8,
    nrad = 68
    npol = 64
    rad_peaking = [0.0]
    pol_peaking = [0.0]
    parweight = 0.0

    boundary = np.array([rz_grid.rlim, rz_grid.zlim])

    # Psi normalisation factors

    faxis = critical.opt_f[critical.primary_opt]

    fnorm = critical.xpt_f[critical.inner_sep] - critical.opt_f[
        critical.primary_opt]

    # From normalised psi, get range of f
    f_inner = faxis + np.min(psi_inner) * fnorm
    f_outer = faxis + np.max(psi_outer) * fnorm

    fvals = radial_grid(nrad, f_inner, f_outer, 1, 1, [xpt_f[inner_sep]],
                        rad_peaking)

    ## Create a starting surface
    #sind = np.int(nrad / 2)
    #start_f = 0. #fvals[sind]

    # Find where we have rational surfaces
    # define an interpolation of psi(q)

    psiq = np.arange(np.float(g.qpsi.size)) * (
        g.sibdry - g.simagx) / np.float(g.qpsi.size - 1) + g.simagx
    fpsiq = interpolate.interp1d(g.qpsi, psiq)

    # Find how many rational surfaces we have within the boundary and locate x,y position of curves

    nmax = g.qpsi.max().astype(int)
    nmin = g.qpsi.min().astype(int)

    nr = np.arange(nmin + 1, nmax + 1)
    psi = fpsiq(nr)

    cs = contour(g.r, g.z, g.psi, levels=psi)
    labels = ['$q=' + np.str(x) + '$\n' for x in range(nr[0], nr[-1] + 1)]

    for i in range(len(labels)):
        cs.collections[i].set_label(labels[i])

    style = ['--', ':', '--', ':', '-.']

    #    gca().set_color_cycle(col)

    #    proxy = [Rectangle((0,0),1,1, fc=col[:i+1])
    #        for pc in cs.collections]
    #
    #    l2=legend(proxy, textstr[:i)
    #
    #    gca().add_artist(l1)

    x = []
    y = []
    for i in range(psi.size):
        xx, yy = surface(cs, i, psi[i], opt_ri[primary_opt],
                         opt_zi[primary_opt], style, option)
        x.append(xx)
        y.append(yy)

    #props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
    #textstr = ['$q='+np.str(x)+'$\n' for x in range(psi.size)]
    #
    #ax.text(0.85, 1.15, ''.join(textstr), transform=ax.transAxes, fontsize=14,
    #verticalalignment='top', bbox=props)

    legend(bbox_to_anchor=(-.8, 1), loc='upper left', borderaxespad=0.)

    draw()

    #compute B - field

    Bp = np.gradient(g.psi)

    dr = old_div((np.max(g.r[:, 0]) - np.min(g.r[:, 0])), np.size(g.r[:, 0]))
    dz = old_div((np.max(g.z[0, :]) - np.min(g.z[0, :])), np.size(g.z[0, :]))

    dpsidr = Bp[0]
    dpsidz = Bp[1]

    Br = -dpsidz / dz / g.r
    Bz = dpsidr / dr / g.r

    Bprz = np.sqrt(Br * Br + Bz * Bz)

    # plot Bp field
    if option == 0:
        sm = query_yes_no("Overplot vector field")
        if sm == 1:
            lw = 50 * Bprz / Bprz.max()
            streamplot(
                g.r.T,
                g.z.T,
                Br.T,
                Bz.T,
                color=Bprz,
                linewidth=2,
                cmap=cm.bone)  #density =[.5, 1], color='k')#, linewidth=lw)
            draw()

    # plot toroidal field

    ax = subplot2grid((3, 3), (0, 1), colspan=2, rowspan=1)
    ax.plot(psiq, fpsi[1, :])
    #ax.set_xlim([0,6])
    #ax.set_xlabel('$\psi$')
    ax.set_ylabel('$fpol$')
    ax.yaxis.label.set_size(20)
    #ax.xaxis.label.set_size(20)

    ax.set_xticks([])

    ax.yaxis.label.set_rotation('horizontal')
    #ax.xaxis.labelpad = 10
    ax.yaxis.labelpad = 20

    #props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
    #ax.text(0.85, 0.95, '$B_t$', transform=ax.transAxes, fontsize=14,
    #    verticalalignment='top', bbox=props)

    #
    draw()

    # plot pressure

    ax = subplot2grid((3, 3), (1, 1), colspan=2, rowspan=1)
    ax.plot(psiq, g.pres)
    #ax.set_xlim([0,6])
    #ax.set_xlabel('$\psi$')
    ax.set_ylabel('$P$')
    ax.yaxis.label.set_size(20)
    #ax.xaxis.label.set_size(20)

    ax.set_xticks([])

    ax.yaxis.label.set_rotation('horizontal')
    #ax.xaxis.labelpad = 10
    ax.yaxis.labelpad = 20

    #props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
    #ax.text(0.85, 0.95, '$B_t$', transform=ax.transAxes, fontsize=14,
    #    verticalalignment='top', bbox=props)

    #
    draw()

    # plot qpsi

    ax = subplot2grid((3, 3), (2, 1), colspan=2, rowspan=1)

    ax.plot(psiq, g.qpsi)
    ax.set_xlabel('$\psi$')
    ax.set_ylabel('$q$')
    ax.yaxis.label.set_rotation('horizontal')
    ax.yaxis.label.set_size(20)
    ax.xaxis.label.set_size(20)

    ax.xaxis.labelpad = 10
    ax.yaxis.labelpad = 20

    tick_params(\
    axis='x',          # changes apply to the x-axis
    which='both',      # both major and minor ticks are affected
    bottom='on',      # ticks along the bottom edge are off
    top='off',         # ticks along the top edge are off
    labelbottom='on')

    draw()

    # Compute and draw Jpar
    #
    #    MU = 4.e-7*np.pi
    #
    #    jpar0 = - Bxy * fprime / MU - Rxy*Btxy * dpdpsi / Bxy
    #

    # fig.set_tight_layout(True)

    fig.subplots_adjust(left=0.2, top=0.9, hspace=0.1, wspace=0.5)

    draw()

    if option == 0: show(block=False)

    if option != 0:
        return Br, Bz, x, y, psi
示例#10
0
print("spsolve() time = %1.6s" % (time.time() - start_time))
#==============================================================================

#==============================================================================
# Calculating the gradient of the solution
vx = -(Dx_2d * u.ravel()).reshape(Ny, Nx)
vy = -(Dy_2d * u.ravel()).reshape(Ny, Nx)
v = np.sqrt(vx**2 + vy**2)
#==============================================================================

#//////////////////////////////////////////////////////////////////////////////
#//////////////////////////////////////////////////////////////////////////////
#//////////////////////////////////////////////////////////////////////////////

#==============================================================================
# Plot solution

py.figure(figsize=(12, 7))
my_contourf(x, y, u, r'$u\,(x,y)$')

# Plotting the gradient
py.figure(figsize=(12, 7))
my_contourf(x, y, v, r'$|-\nabla u\,(x,y)|$', 'afmhot')
py.streamplot(x, y, vx, vy, color='w', density=1.2, linewidth=0.4)

# thin_factor = 10
# skip = (slice(None, None, thin_factor), slice(None, None, thin_factor))
# py.quiver(X[skip],Y[skip],vx[skip]/v[skip],vy[skip]/v[skip],color = 'w')

#==============================================================================
示例#11
0
    delta_n = density - np.mean(density)

    pl.contourf(q1,
                q2,
                delta_n,
                200,
                norm=colors.SymLogNorm(linthresh=delta_n.max() / 20),
                cmap='bwr',
                transform=rot + base)

    pl.streamplot(q1,
                  q2,
                  vel_drift_x,
                  vel_drift_y,
                  density=1.4 * l,
                  color='black',
                  linewidth=1,
                  arrowsize=1.5,
                  transform=rot + base)

    pl.yticks([0, 5.])
    pl.xticks([])

    pl.xlim([q1[0], q1[-1]])
    pl.ylim([q2[0], q2[-1]])

    pl.gca().set_aspect('equal')

    #pl.contourf(q1_meshgrid, q2_meshgrid, density.T, 100, cmap='bwr')
    #pl.title(r'Time = ' + "%.2f"%(time_array[file_number]) + " ps")
示例#12
0
文件: movie.py 项目: mchandra/Bolt
    pl.contourf(x,
                y,
                density.T,
                100,
                norm=MidpointNormalize(midpoint=0,
                                       vmin=density_min,
                                       vmax=density_max),
                cmap='bwr')
    pl.colorbar()
    pl.title(r'Time = ' + "%.2f" % (time_array[start_index + file_number]) +
             " ps")

    pl.streamplot(x[:, 0],
                  y[0, :],
                  j_x,
                  j_y,
                  density=2,
                  color='k',
                  linewidth=0.7,
                  arrowsize=1)

    #    print (j_x)

    pl.xlim([q1[0], q1[-1]])
    pl.ylim([q2[0], q2[-1]])

    pl.gca().set_aspect('equal')
    pl.xlabel(r'$x\;(\mu \mathrm{m})$')
    pl.ylabel(r'$y\;(\mu \mathrm{m})$')
    #pl.suptitle('$\\tau_\mathrm{mc} = \infty$, $\\tau_\mathrm{mr} = \infty$')
    pl.savefig('images/dump_' + '%06d' % (start_index + file_number) + '.png')
    pl.clf()
示例#13
0
def velocity_image(sim,
                   width="10 kpc",
                   vector_color='black',
                   edgecolor='black',
                   vector_resolution=40,
                   scale=None,
                   mode='quiver',
                   key_x=0.3,
                   key_y=0.9,
                   key_color='white',
                   key_length="100 km s**-1",
                   density=1.0,
                   **kwargs):
    """

    Make an SPH image of the given simulation with velocity vectors overlaid on top.

    For a description of additional keyword arguments see :func:`~pynbody.plot.sph.image`,
    or see the `tutorial <http://pynbody.github.io/pynbody/tutorials/pictures.html#velocity-vectors>`_.

    **Keyword arguments:**

    *vector_color* (black): The color for the velocity vectors

    *edgecolor* (black): edge color used for the lines - using a color
     other than black for the *vector_color* and a black *edgecolor*
     can result in poor readability in pdfs

    *vector_resolution* (40): How many vectors in each dimension (default is 40x40)

    *scale* (None): The length of a vector that would result in a displayed length of the
    figure width/height.

    *mode* ('quiver'): make a 'quiver' or 'stream' plot

    *key_x* (0.3): Display x (width) position for the vector key (quiver mode only)

    *key_y* (0.9): Display y (height) position for the vector key (quiver mode only)

    *key_color* (white): Color for the vector key (quiver mode only)

    *key_length* (100 km/s): Velocity to use for the vector key (quiver mode only)

    *density* (1.0): Density of stream lines (stream mode only)

    """

    subplot = kwargs.get('subplot', False)
    if subplot:
        p = subplot
    else:
        import matplotlib.pylab as p

    vx = image(sim,
               qty='vx',
               width=width,
               log=False,
               resolution=vector_resolution,
               noplot=True)
    vy = image(sim,
               qty='vy',
               width=width,
               log=False,
               resolution=vector_resolution,
               noplot=True)
    key_unit = _units.Unit(key_length)

    if isinstance(width, str) or issubclass(width.__class__, _units.UnitBase):
        if isinstance(width, str):
            width = _units.Unit(width)
        width = width.in_units(sim['pos'].units, **sim.conversion_context())

    width = float(width)

    X, Y = np.meshgrid(
        np.arange(-width / 2, width / 2, width / vector_resolution),
        np.arange(-width / 2, width / 2, width / vector_resolution))

    im = image(sim, width=width, **kwargs)

    if mode == 'quiver':
        if scale is None:
            Q = p.quiver(X, Y, vx, vy, color=vector_color, edgecolor=edgecolor)
        else:
            Q = p.quiver(X,
                         Y,
                         vx,
                         vy,
                         scale=_units.Unit(scale).in_units(sim['vel'].units),
                         color=vector_color,
                         edgecolor=edgecolor)
        p.quiverkey(Q,
                    key_x,
                    key_y,
                    key_unit.in_units(sim['vel'].units,
                                      **sim.conversion_context()),
                    r"$\mathbf{" + key_unit.latex() + "}$",
                    labelcolor=key_color,
                    color=key_color,
                    fontproperties={'size': 16})
    elif mode == 'stream':
        Q = p.streamplot(X, Y, vx, vy, color=vector_color, density=density)

    return im
示例#14
0
import pylab as p

# Make an x,z grid
grid = np.linspace(-6,6,200)
x, z = np.meshgrid(grid,grid)
mu=4*np.pi  # dipole strength (in z-direction)
# and the 2D cross section of the potential (scalar field)
phi = mu * z / (4 * np.pi * (x**2+z**2)**(3/2))
# End initialization

# Start contour plot
from matplotlib import colors
p.figure(figsize=(6,6))
heights = np.linspace(-0.6,0.6,7)
CS = p.contour(x,z,phi,heights,colors='k')
p.clabel(CS, inline=1, fontsize=10,fmt='%3.1f')
p.xlabel(r'$x$')
p.ylabel(r'$z$')
# End contour plot

# Start fieldline plot
Fx = mu / (4 * np.pi * (x**2+z**2)**(5/2)) * 2*x*z
Fz = mu / (4 * np.pi * (x**2+z**2)**(5/2)) * (z**2-x**2)
p.streamplot(x,z,Fx,Fz)
# End fieldline plot
CS = p.contour(x,z,phi,heights,colors='k')
p.clabel(CS, inline=1, fontsize=10,fmt='%3.1f')
p.savefig('dipole_fieldplot.png')

#p.show()
示例#15
0
# contor grid
res = 0.4
xlim, zlim = ([-10,10], [-5,10])
dx, dz = (xlim[1]-xlim[0], zlim[1]-zlim[0])
nx, nz = (int(dx/res), int(dz/res))

x = np.linspace(xlim[0], xlim[1], nx)
z = np.linspace(zlim[0], zlim[1], nz)
xm, zm = np.meshgrid(x, z)
feild = np.zeros((nz,nx))
Bx = np.zeros((nz,nx))
Bz = np.zeros((nz,nx))
for i in range(nz):
    for j in range(nx):
        B = Bcoil(coil, [xm[i,j],zm[i,j]])
        Bx[i,j] = B[0]
        Bz[i,j] = B[2]
        #feild[i,j] = B[2]#sum(B*B)**0.5
        feild[i,j] = sum(B*B)**0.5

        
pl.figure(figsize=(12,12))
V = np.linspace(0,2,30)
#pl.contour(xm, zm, feild,V)
pl.plot(xm,zm,'k.')
pl.quiver(xm, zm, Bx, Bz)
pl.streamplot(xm, zm, Bx, Bz)
pl.axis('equal')
pl.xlim(xlim)
pl.ylim(zlim)
示例#16
0
CS = py.contour(xi, yi, Fi, levels=levels, colors='k', linewidths=linewidth)
clabels = py.clabel(CS, levels,
    inline=True,
    use_clabeltext=True,
    fmt='%g',
    manual=labelpos,
    fontsize=fontsize)
[ txt.set_backgroundcolor('white') for txt in clabels ]
[ txt.set_bbox(dict(facecolor='white', edgecolor='none', pad=0)) for txt in clabels ]

### Add a streamplot for the vector field
# NB: py.streamplot relies on evenly grid
if len(F.shape) > 1:
    py.streamplot(xi, yi, Ui, Vi,
        density=0.89,
        minlength=.2,
        arrowstyle='->',
        color='k',
        linewidth=1)

py.text(-.05, .4, r'$\displaystyle %s$' % args.latex)
py.xlabel(r'$x$', labelpad=-5)
py.ylabel(r'$y$', labelpad=-5, rotation=0)
py.xlim(xmin, xmax)
py.ylim(ymin, ymax)
ax = py.axes()
ax.set_aspect('equal')
ax.set_yticks([xmin, xmax])
ax.set_xticks([ymin, ymax])
py.tick_params(axis='both', direction='out')
py.savefig(args.pdffile, bbox_inches='tight', transparent=True)
示例#17
0
import numpy as np
import pylab as pl
"""
A = np.array([[-1, 2], [2, -2]])
eig = np.linalg.eig(A)[0]
eigV = np.linalg.eig(A)[1]

x, y = np.meshgrid(np.arange(0, 3, 0.1), np.arange(0, 3, 0.1))

xv = -x + 2*y
yv = 2*x - 2*y

pl.streamplot(x, y, xv, yv)
pl.plot([0,0.7882], [0, 0.6154], 'r')

#######################
# LATER ON IN LECTURE #
#######################

from sympy import symbols, Matrix

a, b, c, d, x, y = symbols('a b c d x y')

# a, b, c, d > 0

dx = a*x - b*x*y
dy = -c*y + d*x*y

J = Matrix(
        [[dx.diff(x), dx.diff(y)], # column 1
        [dy.diff(x), dy.diff(y)]]) # column 2
示例#18
0
文件: movie.py 项目: mchandra/Bolt
    density = density - density_bg
    density_min = np.min(density)
    density_max = np.max(density)

    #plot_grid(x[::5, ::5], y[::5, ::5], alpha=0.5)
    #pl.contourf(x, y, density.T, 100, cmap='bwr')
    #pl.contourf(x, y, density.T, 100, norm=MidpointNormalize(midpoint=0, vmin=density_min, vmax=density_max), cmap='bwr')
    #pl.contourf(x, y, density.T, 1000, norm=colors.DivergingNorm(vcenter=0, vmin=density_min, vmax=density_max), cmap='bwr')
    #pl.colorbar()
    #pl.title(r'Time = ' + "%.2f"%(time_array[start_index+file_number]) + " ps")

    pl.streamplot(x[:, 0],
                  y[0, :],
                  vel_drift_x,
                  vel_drift_y,
                  density=4.,
                  color='k',
                  linewidth=1.5,
                  arrowsize=1.2)

    #pl.xlim([q1[0], q1[-1]])
    #pl.ylim([q2[0], q2[-1]])

    pl.xlim([0.5, 1.5])
    pl.ylim([q2[0], 0.625])

    pl.gca().spines['right'].set_visible(False)
    pl.gca().spines['top'].set_visible(False)
    pl.gca().spines['bottom'].set_visible(False)
    pl.gca().spines['left'].set_visible(False)
示例#19
0
文件: velocity.py 项目: olegrog/latex
    M = X.max()
    X = np.reshape(X,(N,N)) * L/M
    Y = np.reshape(Y,(N,N)) * L/M
    x,y = X[0,:], Y[:,0]
    U = np.reshape(U/rho,(N,N)) / k 
    V = np.reshape(V/rho,(N,N)) / k
    return U,V,x,y

data = py.loadtxt(sys.argv[2]).T
U,V,x,y = load_data()

magU = np.sqrt(U*U+V*V)
cmap = py.cm.get_cmap('coolwarm')
CF = py.contourf(x, y, magU, cmap=cmap)
py.colorbar(CF, use_gridspec=True)

# streamplot version (need matplotlib 1.2.1, use only evenly grid)
py.streamplot(x, y, U, V, color='k', density=.8, minlength=.4)

py.text(-.06, .37, r'$\displaystyle\frac{u_i}k$', fontsize=11)
py.xlabel(r'$x$', labelpad=-5)
py.ylabel(r'$z$', labelpad=-5, rotation=0)
py.xlim(0,0.5)
py.ylim(0,0.5)
ax = py.axes()
ax.set_aspect('equal')
ax.set_yticks([0,0.5])
ax.set_xticks([0,0.5])
py.tick_params(axis='both', direction='out')
py.savefig(sys.argv[3], bbox_inches='tight')
示例#20
0
def velocity_image(sim,
                   width="10 kpc",
                   vector_color='black',
                   edgecolor='black',
                   quiverkey_bg_color=None,
                   vector_resolution=40,
                   scale=None,
                   mode='quiver',
                   key_x=0.3,
                   key_y=0.9,
                   key_color='white',
                   key_length="100 km s**-1",
                   quiverkey=True,
                   density=1.0,
                   vector_qty='vel',
                   **kwargs):
    """

    Make an SPH image of the given simulation with velocity vectors overlaid on top.

    For a description of additional keyword arguments see :func:`~pynbody.plot.sph.image`,
    or see the `tutorial <http://pynbody.github.io/pynbody/tutorials/pictures.html#velocity-vectors>`_.

    **Keyword arguments:**

    *vector_color* (black): The color for the velocity vectors

    *edgecolor* (black): edge color used for the lines - using a color
     other than black for the *vector_color* and a black *edgecolor*
     can result in poor readability in pdfs

    *vector_resolution* (40): How many vectors in each dimension (default is 40x40)

    *quiverkey_bg_color* (none): The color for the legend (scale) background

    *scale* (None): The length of a vector that would result in a displayed length of the
    figure width/height.

    *mode* ('quiver'): make a 'quiver' or 'stream' plot

    *key_x* (0.3): Display x (width) position for the vector key (quiver mode only)

    *key_y* (0.9): Display y (height) position for the vector key (quiver mode only)

    *key_color* (white): Color for the vector key (quiver mode only)

    *key_length* (100 km/s): Velocity to use for the vector key (quiver mode only)

    *density* (1.0): Density of stream lines (stream mode only)

    *quiverkey* (True): Whether or not to inset the key

    *vector_qty* ('vel'): The name of the vector field to plot
    """

    subplot = kwargs.get('subplot', False)
    av_z = kwargs.get('av_z', None)
    if subplot:
        p = subplot
    else:
        import matplotlib.pylab as p

    vx_name, vy_name, _ = sim._array_name_ND_to_1D(vector_qty)

    vx = image(sim,
               qty=vx_name,
               width=width,
               log=False,
               resolution=vector_resolution,
               noplot=True,
               av_z=av_z)
    vy = image(sim,
               qty=vy_name,
               width=width,
               log=False,
               resolution=vector_resolution,
               noplot=True,
               av_z=av_z)
    key_unit = _units.Unit(key_length)

    if isinstance(width, str) or issubclass(width.__class__, _units.UnitBase):
        if isinstance(width, str):
            width = _units.Unit(width)
        width = width.in_units(sim['pos'].units, **sim.conversion_context())

    width = float(width)

    pixel_size = width / vector_resolution
    X, Y = np.meshgrid(
        np.arange(-width / 2 + pixel_size / 2, width / 2 + pixel_size / 2,
                  pixel_size),
        np.arange(-width / 2 + pixel_size / 2, width / 2 + pixel_size / 2,
                  pixel_size))

    im = image(sim, width=width, **kwargs)

    if mode == 'quiver':
        if scale is None:
            Q = p.quiver(X, Y, vx, vy, color=vector_color, edgecolor=edgecolor)
        else:
            Q = p.quiver(X,
                         Y,
                         vx,
                         vy,
                         scale=_units.Unit(scale).in_units(sim['vel'].units),
                         color=vector_color,
                         edgecolor=edgecolor)
        if quiverkey:
            qk = p.quiverkey(Q,
                             key_x,
                             key_y,
                             key_unit.in_units(sim['vel'].units,
                                               **sim.conversion_context()),
                             r"$\mathbf{" + key_unit.latex() + "}$",
                             labelcolor=key_color,
                             color=key_color,
                             fontproperties={'size': 24})
        if quiverkey_bg_color is not None:
            qk.text.set_backgroundcolor(quiverkey_bg_color)
    elif mode == 'stream':
        Q = p.streamplot(X, Y, vx, vy, color=vector_color, density=density)

# RS - if a axis object is passed in, use the right limit call
    if subplot:
        p.set_xlim(-width / 2, width / 2)
        p.set_ylim(-width / 2, width / 2)
    else:
        p.xlim(-width / 2, width / 2)
        p.ylim(-width / 2, width / 2)

    return im
示例#21
0
r = np.linspace(rlim[0], rlim[1], nr)
z = np.linspace(zlim[0], zlim[1], nz)
rm, zm = np.meshgrid(r, z)
Br = np.zeros((nz, nr))
Bz = np.zeros((nz, nr))
Bmag = np.zeros((nz, nr))
for i in range(nz):
    for j in range(nr):
        B = Bcoil(mu_o, coil, [rm[i, j], zm[i, j]])
        Br[i, j] = B[0]
        Bz[i, j] = B[1]
        Bmag[i, j] = sum(B * B)**0.5

V = np.linspace(0, 10, 30)
pl.streamplot(rm, zm, Br, Bz, density=[2 * nr / 25, 2 * nz / 25])
pl.contour(rm, zm, Bmag, levels=[5 * np.min(Bmag)])
pl.axis('equal')
pl.xlim(rlim)
pl.ylim(zlim)

if I > 0:
    plasma_dir = 'pos'
elif I == 0:
    plasma_dir = 'off'
else:
    plasma_dir = 'neg'
#pl.savefig(fig_path+config+'_flux_conv_'+plasma_dir+'.png', dpi=300)

fig = pl.figure(figsize=(9, 12))
ax = fig.add_subplot(111)
示例#22
0
文件: movie.py 项目: mchandra/Bolt
    mu = lagrange_multipliers[:, :, 0]
    mu_ee = lagrange_multipliers[:, :, 1]
    T_ee = lagrange_multipliers[:, :, 2]
    vel_drift_x = lagrange_multipliers[:, :, 3]
    vel_drift_y = lagrange_multipliers[:, :, 4]

    print(vel_drift_x.shape)
    print(density.shape)

    pl.contourf(q1_meshgrid, q2_meshgrid, density.T, 100, cmap='bwr')
    pl.title(r'Time = ' + "%.2f" % (time_array[file_number]) + " ps")
    pl.streamplot(q1,
                  q2,
                  vel_drift_x,
                  vel_drift_y,
                  density=2,
                  color='k',
                  linewidth=0.7,
                  arrowsize=1)

    pl.xlim([q1[0], q1[-1]])
    pl.ylim([q2[0], q2[-1]])

    pl.gca().set_aspect('equal')
    pl.xlabel(r'$x\;(\mu \mathrm{m})$')
    pl.ylabel(r'$y\;(\mu \mathrm{m})$')
    pl.suptitle('$\\tau_\mathrm{mc} = 0.2$ ps, $\\tau_\mathrm{mr} = 1.0$ ps')
    pl.savefig('images/dump_' + '%06d' % file_number + '.png')
    pl.clf()
示例#23
0
	ax  = fig.gca()

	if value == 'Density':
		data = rawData[:,:,0,t]
	if value == 'Velocity':
		Vx = rawData[:,:,2,t]/rawData[:,:,0,t] # x velocity
		Vy = rawData[:,:,1,t]/rawData[:,:,0,t] # y velocity (seem reversed cause of poor definitions in earlier code
		data = np.sqrt(Vx*Vx+Vy*Vy) #total speed
	#	print data
		X, Y =  np.linspace(-0.5,0.5,Nx), np.linspace(0,5,Ny) ## CHANGE THIS MANUALLY FOR DIFFERENT SYSTEMS so far

	
	if value =='Density':
		plt.imshow(data, origin = 'lower',cmap = colormap)
	if value == 'Velocity':
		plt.streamplot(X, Y, Vx, Vy ,color='k', linewidth=2)
	plt.tick_params(labelsize=tickSize)
	plt.title(title, fontsize = titleSize)
	if value == 'Density':
		plt.xlabel('X Cell Number', fontsize = labelSize)
		plt.ylabel('Y Cell Number', fontsize = labelSize)
		plt.xlim([3,Nx-2])
		plt.ylim([3,Ny-2])
		m = cm.ScalarMappable(cmap= colormap)
		m.set_array(data)
		plt.colorbar(m)
	if value == 'Velocity':
		plt.xlabel('X Position', fontsize = labelSize)
		plt.ylabel('Y Position', fontsize = labelSize)
		plt.xlim([min(X),max(X)])
		plt.ylim([min(Y),max(Y)])
示例#24
0
文件: sph.py 项目: migueldvb/pynbody
def velocity_image(sim, width="10 kpc", vector_color='black', edgecolor='black', quiverkey_bg_color=None,
                   vector_resolution=40, scale=None, mode='quiver', key_x=0.3, key_y=0.9,
                   key_color='white', key_length="100 km s**-1", quiverkey=True, density=1.0, **kwargs):
    """

    Make an SPH image of the given simulation with velocity vectors overlaid on top.

    For a description of additional keyword arguments see :func:`~pynbody.plot.sph.image`,
    or see the `tutorial <http://pynbody.github.io/pynbody/tutorials/pictures.html#velocity-vectors>`_.

    **Keyword arguments:**

    *vector_color* (black): The color for the velocity vectors

    *edgecolor* (black): edge color used for the lines - using a color
     other than black for the *vector_color* and a black *edgecolor*
     can result in poor readability in pdfs

    *vector_resolution* (40): How many vectors in each dimension (default is 40x40)

    *quiverkey_bg_color* (none): The color for the legend (scale) background

    *scale* (None): The length of a vector that would result in a displayed length of the
    figure width/height.

    *mode* ('quiver'): make a 'quiver' or 'stream' plot

    *key_x* (0.3): Display x (width) position for the vector key (quiver mode only)

    *key_y* (0.9): Display y (height) position for the vector key (quiver mode only)

    *key_color* (white): Color for the vector key (quiver mode only)

    *key_length* (100 km/s): Velocity to use for the vector key (quiver mode only)

    *density* (1.0): Density of stream lines (stream mode only)

    *quiverkey* (True): Whether or not to inset the key

    """

    subplot = kwargs.get('subplot', False)
    av_z = kwargs.get('av_z',None)
    if subplot:
        p = subplot
    else:
        import matplotlib.pylab as p

    vx = image(sim, qty='vx', width=width, log=False,
               resolution=vector_resolution, noplot=True,av_z=av_z)
    vy = image(sim, qty='vy', width=width, log=False,
               resolution=vector_resolution, noplot=True,av_z=av_z)
    key_unit = _units.Unit(key_length)

    if isinstance(width, str) or issubclass(width.__class__, _units.UnitBase):
        if isinstance(width, str):
            width = _units.Unit(width)
        width = width.in_units(sim['pos'].units, **sim.conversion_context())

    width = float(width)

    X, Y = np.meshgrid(np.arange(-width / 2, width / 2, width / vector_resolution),
                       np.arange(-width / 2, width / 2, width / vector_resolution))

    im = image(sim, width=width, **kwargs)

    if mode == 'quiver':
        if scale is None:
            Q = p.quiver(X, Y, vx, vy, color=vector_color, edgecolor=edgecolor)
        else:
            Q = p.quiver(X, Y, vx, vy, scale=_units.Unit(scale).in_units(
                sim['vel'].units), color=vector_color, edgecolor=edgecolor)
        if quiverkey:
        	qk = p.quiverkey(Q, key_x, key_y, key_unit.in_units(sim['vel'].units, **sim.conversion_context()),
                    r"$\mathbf{" + key_unit.latex() + "}$", labelcolor=key_color, color=key_color, fontproperties={'size': 24})
        if  quiverkey_bg_color is not None:
            qk.text.set_backgroundcolor(quiverkey_bg_color)
    elif mode == 'stream' :
        Q = p.streamplot(X, Y, vx, vy, color=vector_color, density=density)

    return im
示例#25
0
def View2D(g, option=0):

    # plot and check the field
    fig = figure(num=2, figsize=(16, 6))
    # fig.suptitle('Efit Analysis', fontsize=20)

    ax = subplot2grid((3, 3), (0, 0), colspan=1, rowspan=3)
    nlev = 100
    minf = np.min(g.psi)
    maxf = np.max(g.psi)
    levels = np.arange(np.float(nlev)) * (maxf - minf) / np.float(nlev - 1) + minf
    ax.contour(g.r, g.z, g.psi, levels=levels)
    #    ax.set_xlim([0,6])
    ax.set_xlabel("R")
    ax.set_ylabel("Z")
    ax.yaxis.label.set_rotation("horizontal")

    ax.set_aspect("equal")

    # fig.suptitle('Efit Analysis', fontsize=20)
    # title('Efit Analysis', fontsize=20)
    text(0.5, 1.08, "Efit Analysis", horizontalalignment="center", fontsize=20, transform=ax.transAxes)

    draw()
    if option == 0:
        show(block=False)

    plot(g.xlim, g.ylim, "g-")

    draw()

    csb = contour(g.r, g.z, g.psi, levels=[g.sibdry])

    clabel(csb, [g.sibdry], inline=1, fmt="%9.6f", fontsize=14)  # label the level

    csb.collections[0].set_label("boundary")

    # pl1=plot(g.rbdry,g.zbdry,'b-',marker='x', label='$\psi=$'+ np.str(g.sibdry))
    #  legend(bbox_to_anchor=(0., 1.05, 1., .105), loc='upper left')

    draw()

    # fig.set_tight_layout(True)

    #  show(block=False)

    # Function fpol and qpsi are given between simagx (psi on the axis) and sibdry (
    # psi on limiter or separatrix). So the toroidal field (fpol/R) and the q profile are within these boundaries

    npsigrid = old_div(np.arange(np.size(g.pres)).astype(float), (np.size(g.pres) - 1))

    fpsi = np.zeros((2, np.size(g.fpol)), np.float64)
    fpsi[0, :] = g.simagx + npsigrid * (g.sibdry - g.simagx)
    fpsi[1, :] = g.fpol

    boundary = np.array([g.xlim, g.ylim])

    rz_grid = Bunch(
        nr=g.nx,
        nz=g.ny,  # Number of grid points
        r=g.r[:, 0],
        z=g.z[0, :],  # R and Z as 1D arrays
        simagx=g.simagx,
        sibdry=g.sibdry,  # Range of psi
        psi=g.psi,  # Poloidal flux in Weber/rad on grid points
        npsigrid=npsigrid,  # Normalised psi grid for fpol, pres and qpsi
        fpol=g.fpol,  # Poloidal current function on uniform flux grid
        pres=g.pres,  # Plasma pressure in nt/m^2 on uniform flux grid
        qpsi=g.qpsi,  # q values on uniform flux grid
        nlim=g.nlim,
        rlim=g.xlim,
        zlim=g.ylim,
    )  # Wall boundary

    critical = analyse_equil(g.psi, g.r[:, 0], g.z[0, :])

    n_opoint = critical.n_opoint
    n_xpoint = critical.n_xpoint
    primary_opt = critical.primary_opt
    inner_sep = critical.inner_sep
    opt_ri = critical.opt_ri
    opt_zi = critical.opt_zi
    opt_f = critical.opt_f
    xpt_ri = critical.xpt_ri
    xpt_zi = critical.xpt_zi
    xpt_f = critical.xpt_f

    psi_inner = 0.6
    psi_outer = (0.8,)
    nrad = 68
    npol = 64
    rad_peaking = [0.0]
    pol_peaking = [0.0]
    parweight = 0.0

    boundary = np.array([rz_grid.rlim, rz_grid.zlim])

    # Psi normalisation factors

    faxis = critical.opt_f[critical.primary_opt]

    fnorm = critical.xpt_f[critical.inner_sep] - critical.opt_f[critical.primary_opt]

    # From normalised psi, get range of f
    f_inner = faxis + np.min(psi_inner) * fnorm
    f_outer = faxis + np.max(psi_outer) * fnorm

    fvals = radial_grid(nrad, f_inner, f_outer, 1, 1, [xpt_f[inner_sep]], rad_peaking)

    ## Create a starting surface
    # sind = np.int(nrad / 2)
    # start_f = 0. #fvals[sind]

    # Find where we have rational surfaces
    # define an interpolation of psi(q)

    psiq = np.arange(np.float(g.qpsi.size)) * (g.sibdry - g.simagx) / np.float(g.qpsi.size - 1) + g.simagx
    fpsiq = interpolate.interp1d(g.qpsi, psiq)

    # Find how many rational surfaces we have within the boundary and locate x,y position of curves

    nmax = g.qpsi.max().astype(int)
    nmin = g.qpsi.min().astype(int)

    nr = np.arange(nmin + 1, nmax + 1)
    psi = fpsiq(nr)

    cs = contour(g.r, g.z, g.psi, levels=psi)
    labels = ["$q=" + np.str(x) + "$\n" for x in range(nr[0], nr[-1] + 1)]

    for i in range(len(labels)):
        cs.collections[i].set_label(labels[i])

    style = ["--", ":", "--", ":", "-."]

    #    gca().set_color_cycle(col)

    #    proxy = [Rectangle((0,0),1,1, fc=col[:i+1])
    #        for pc in cs.collections]
    #
    #    l2=legend(proxy, textstr[:i)
    #
    #    gca().add_artist(l1)

    x = []
    y = []
    for i in range(psi.size):
        xx, yy = surface(cs, i, psi[i], opt_ri[primary_opt], opt_zi[primary_opt], style, option)
        x.append(xx)
        y.append(yy)

    # props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
    # textstr = ['$q='+np.str(x)+'$\n' for x in range(psi.size)]
    #
    # ax.text(0.85, 1.15, ''.join(textstr), transform=ax.transAxes, fontsize=14,
    # verticalalignment='top', bbox=props)

    legend(bbox_to_anchor=(-0.8, 1), loc="upper left", borderaxespad=0.0)

    draw()

    # compute B - field

    Bp = np.gradient(g.psi)

    dr = old_div((np.max(g.r[:, 0]) - np.min(g.r[:, 0])), np.size(g.r[:, 0]))
    dz = old_div((np.max(g.z[0, :]) - np.min(g.z[0, :])), np.size(g.z[0, :]))

    dpsidr = Bp[0]
    dpsidz = Bp[1]

    Br = -dpsidz / dz / g.r
    Bz = dpsidr / dr / g.r

    Bprz = np.sqrt(Br * Br + Bz * Bz)

    # plot Bp field
    if option == 0:
        sm = query_yes_no("Overplot vector field")
        if sm == 1:
            lw = 50 * Bprz / Bprz.max()
            streamplot(
                g.r.T, g.z.T, Br.T, Bz.T, color=Bprz, linewidth=2, cmap=cm.bone
            )  # density =[.5, 1], color='k')#, linewidth=lw)
            draw()

    # plot toroidal field

    ax = subplot2grid((3, 3), (0, 1), colspan=2, rowspan=1)
    ax.plot(psiq, fpsi[1, :])
    # ax.set_xlim([0,6])
    # ax.set_xlabel('$\psi$')
    ax.set_ylabel("$fpol$")
    ax.yaxis.label.set_size(20)
    # ax.xaxis.label.set_size(20)

    ax.set_xticks([])

    ax.yaxis.label.set_rotation("horizontal")
    # ax.xaxis.labelpad = 10
    ax.yaxis.labelpad = 20

    # props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
    # ax.text(0.85, 0.95, '$B_t$', transform=ax.transAxes, fontsize=14,
    #    verticalalignment='top', bbox=props)

    #
    draw()

    # plot pressure

    ax = subplot2grid((3, 3), (1, 1), colspan=2, rowspan=1)
    ax.plot(psiq, g.pres)
    # ax.set_xlim([0,6])
    # ax.set_xlabel('$\psi$')
    ax.set_ylabel("$P$")
    ax.yaxis.label.set_size(20)
    # ax.xaxis.label.set_size(20)

    ax.set_xticks([])

    ax.yaxis.label.set_rotation("horizontal")
    # ax.xaxis.labelpad = 10
    ax.yaxis.labelpad = 20

    # props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
    # ax.text(0.85, 0.95, '$B_t$', transform=ax.transAxes, fontsize=14,
    #    verticalalignment='top', bbox=props)

    #
    draw()

    # plot qpsi

    ax = subplot2grid((3, 3), (2, 1), colspan=2, rowspan=1)

    ax.plot(psiq, g.qpsi)
    ax.set_xlabel("$\psi$")
    ax.set_ylabel("$q$")
    ax.yaxis.label.set_rotation("horizontal")
    ax.yaxis.label.set_size(20)
    ax.xaxis.label.set_size(20)

    ax.xaxis.labelpad = 10
    ax.yaxis.labelpad = 20

    tick_params(
        axis="x",  # changes apply to the x-axis
        which="both",  # both major and minor ticks are affected
        bottom="on",  # ticks along the bottom edge are off
        top="off",  # ticks along the top edge are off
        labelbottom="on",
    )

    draw()

    # Compute and draw Jpar
    #
    #    MU = 4.e-7*np.pi
    #
    #    jpar0 = - Bxy * fprime / MU - Rxy*Btxy * dpdpsi / Bxy
    #

    # fig.set_tight_layout(True)

    fig.subplots_adjust(left=0.2, top=0.9, hspace=0.1, wspace=0.5)

    draw()

    if option == 0:
        show(block=False)

    if option != 0:
        return Br, Bz, x, y, psi
示例#26
0
    density_min = np.min(density)
    density_max = np.max(density)

    ground_indices = y[0, :] < 0.25 
    # Ground the right contact
    #density = density - np.mean(density[ground_indices, -1])

    #plot_grid(x[::5, ::5], y[::5, ::5], alpha=0.5)
    #pl.contourf(x, y, density.T, 100, cmap='bwr')
    pl.contourf(x, y, density.T, 100, norm=MidpointNormalize(midpoint=0, vmin=density_min, vmax=density_max), cmap='bwr')
    pl.title(r'Time = ' + "%.2f"%(time_array[start_index+file_number]) + " ps")
    #pl.colorbar()
    
    pl.streamplot(x[:, 0], y[0, :], 
                  vel_drift_x, vel_drift_y,
                  density=2., color='k',
                  linewidth=0.7, arrowsize=1
                 )

    #pl.axvline(1.5, color = 'k', ls = '--')
    #pl.axvline(3.5, color = 'k', ls = '--')
    pl.axvspan(4.8, 4.9, color = 'k', alpha = 0.3)
    #pl.axvspan(3.45, 3.55, color = 'k', alpha = 0.5)

    pl.xlim([0, 2])
    pl.ylim([0, 1.5])
    
    pl.gca().set_aspect('equal')
    pl.xlabel(r'$x\;(\mu \mathrm{m})$')
    pl.ylabel(r'$y\;(\mu \mathrm{m})$')
#    pl.suptitle('$\\tau_\mathrm{mc} = \infty$, $\\tau_\mathrm{mr} = \infty$')
示例#27
0
plot.coil_loc()

pl.plot(r,z,'b-x')
pl.axis('equal')


# contor grid
res = 0.4
xlim, zlim = ([3,14], [-11,5])
dx, dz = (xlim[1]-xlim[0], zlim[1]-zlim[0])
nx, nz = (int(dx/res), int(dz/res))

x = np.linspace(xlim[0], xlim[1], nx)
z = np.linspace(zlim[0], zlim[1], nz)
xm, zm = np.meshgrid(x, z)
feild = np.zeros((nz,nx))
Bx = np.zeros((nz,nx))
Bz = np.zeros((nz,nx))
for i in range(nz):
    for j in range(nx):
        B = Bcoil(mu_o, coil, [xm[i,j],0,zm[i,j]])
        Bx[i,j] = B[0]
        Bz[i,j] = B[2]
        feild[i,j] = sum(B*B)**0.5

V = np.linspace(0,10,30)
pl.streamplot(xm, zm, Bx, Bz, density = [2*nx/25,2*nz/25])
pl.axis('equal')
pl.xlim(xlim)
pl.ylim(zlim)