amrplot Namespace Reference

Classes
class	polyplot
	Simple 2D plotter class using matplotlib, plots every cell in one patch. More...
class	rgplot
	As polyplot, but use regridded data to display. More...

Functions
def	velovect (u1, u2, d, nvect=None, scalevar=None, scale=100, color='k', ax=None, alpha=1.)
	Plots normalized velocity vectors. More...
def	contour (var, d, levels=None, nmax=600, colors='k', linestyles='solid', ax=None, linewidths=1, smooth=1., alpha=1.)
def	streamlines (u1, u2, d, x0=None, y0=None, nmax=600, density=1, fig=None, color='b', linewidth=1, arrowsize=1, alpha=1., smooth=0)
	plots streamlines from a vector field. More...
def	streamline (u1, u2, d, x1_0, x2_0, dl=1., fig=None, nmax=600)
def	gridvar (var, d, xrange, yrange, nmax=600, smooth=0)

Variables
	default_timer = time.clock

Function Documentation

contour()

def amrplot.contour	(		var,
			d,
			levels = None,
			nmax = 600,
			colors = 'k',
			linestyles = 'solid',
			ax = None,
			linewidths = 1,
			smooth = 1.,
			alpha = 1.
	)

Definition at line 475 of file amrplot.py.

def contour(var,d,levels=None,nmax=600,colors='k',linestyles='solid',ax=None,linewidths=1,smooth=1.,alpha=1.):
    if ax==None:
        ax=plt.gca()

    xrange=[ax.get_xlim()[0],ax.get_xlim()[1]]
    yrange=[ax.get_ylim()[0],ax.get_ylim()[1]]

    # Aspect ratio:
    r=(xrange[1]-xrange[0])/(yrange[1]-yrange[0])
    if r<1:
        ny=nmax
        nx=int(r*nmax)
    else:
        nx=nmax
        ny=int(nmax/r)
    nregrid = [nx,ny]
    
    CC=d.getCenterPoints()
    tmp0=np.complex(0,nregrid[0])
    tmp1=np.complex(0,nregrid[1])
    grid_x, grid_y = np.mgrid[xrange[0]:xrange[1]:tmp0, yrange[0]:yrange[1]:tmp1]
    gridvar = griddata(CC, var, (grid_x, grid_y), method='linear')
    isnan=np.isnan(gridvar)
    gridvar[isnan] = griddata(CC, var, (grid_x[isnan], grid_y[isnan]), method='nearest')

# smooth the data slightly:
    blurred_gridvar = ndimage.gaussian_filter(gridvar, sigma=smooth)


    if levels == None:
        cs = ax.contour(grid_x,grid_y,blurred_gridvar,16,alpha=alpha)
    else:
        cs = ax.contour(grid_x,grid_y,blurred_gridvar,levels=levels,colors=colors,linestyles=linestyles,linewidths=linewidths,alpha=alpha)

    plt.draw()
    return cs

#=============================================================================

gridvar()

def amrplot.gridvar	(		var,
			d,
			xrange,
			yrange,
			nmax = 600,
			smooth = 0
	)

Definition at line 629 of file amrplot.py.

def gridvar(var,d,xrange,yrange,nmax=600,smooth=0):

    # Aspect ratio:
    r=(xrange[1]-xrange[0])/(yrange[1]-yrange[0])
    if r<1:
        ny=nmax
        nx=int(r*nmax)
    else:
        nx=nmax
        ny=int(nmax/r)
    nregrid = [nx,ny]
    
    CC=d.getCenterPoints()
    tmp0=np.complex(0,nregrid[0])
    tmp1=np.complex(0,nregrid[1])
    grid_x, grid_y = np.mgrid[xrange[0]:xrange[1]:tmp0, yrange[0]:yrange[1]:tmp1]
    gridvar = griddata(CC, var, (grid_x, grid_y), method='linear')
    isnan=np.isnan(gridvar)
    gridvar[isnan] = griddata(CC, var, (grid_x[isnan], grid_y[isnan]), method='nearest')

# smooth the data slightly:
    blurred_gridvar = ndimage.gaussian_filter(gridvar, sigma=smooth)

    return blurred_gridvar,grid_x,grid_y

streamline()

def amrplot.streamline	(		u1,
			u2,
			d,
			x1_0,
			x2_0,
			dl = 1.,
			fig = None,
			nmax = 600
	)

Definition at line 571 of file amrplot.py.

def streamline(u1,u2,d,x1_0,x2_0,dl=1.,fig=None,nmax=600):

    if fig==None:
        ax=plt.gca()
    else:
        ax=fig.figure.gca()

    xrange=[ax.get_xlim()[0],ax.get_xlim()[1]]
    yrange=[ax.get_ylim()[0],ax.get_ylim()[1]]

    if nmax == 600 and fig != None:
        nmax = fig.dpi * max([fig.fig_w,fig.fig_h])


    # Aspect ratio:
    r=(xrange[1]-xrange[0])/(yrange[1]-yrange[0])
    if r<1:
        ny=nmax
        nx=int(r*nmax)
    else:
        nx=nmax
        ny=int(nmax/r)
    nregrid = [nx,ny]

    CC=d.getCenterPoints()
    tmp0=np.complex(0,nregrid[0])
    tmp1=np.complex(0,nregrid[1])
    x=np.linspace(xrange[0],xrange[1],nregrid[0])
    y=np.linspace(yrange[0],yrange[1],nregrid[1])
    grid_x, grid_y = np.mgrid[xrange[0]:xrange[1]:tmp0, yrange[0]:yrange[1]:tmp1]

    u = griddata(CC, u1, (grid_x, grid_y), method='linear')
    v = griddata(CC, u2, (grid_x, grid_y), method='linear')
    uisnan=np.isnan(u)
    visnan=np.isnan(v)
    un = np.empty(np.shape(u))
    vn = np.empty(np.shape(v))
    un[uisnan] = griddata(CC, u1, (grid_x[uisnan], grid_y[uisnan]), method='nearest')
    vn[visnan] = griddata(CC, u2, (grid_x[visnan], grid_y[visnan]), method='nearest')
    u[uisnan]=un[uisnan]
    v[visnan]=vn[visnan]
# Normalize:
    mag=np.sqrt(u**2+v**2)
    u=u/mag
    v=v/mag

    fu = interpolate.interp2d(grid_x, grid_y, u, kind='cubic')
    fv = interpolate.interp2d(grid_x, grid_y, v, kind='cubic')
    x1=[x1_0]
    x2=[x2_0]
    while(x1[-1] >= xrange[0] and x1[-1] <= xrange[1] and
    x2[-1] >= yrange[0] and x2[-1] <= yrange[1]):
        dt=dl
        x1.append(x1[-1]+fu(x1[-1],x2[-1])*dt)
        x2.append(x2[-1]+fv(x1[-1],x2[-1])*dt)
    return [np.array(x1),np.array(x2)]

#=============================================================================

streamlines()

def amrplot.streamlines	(		u1,
			u2,
			d,
			x0 = None,
			y0 = None,
			nmax = 600,
			density = 1,
			fig = None,
			color = 'b',
			linewidth = 1,
			arrowsize = 1,
			alpha = 1.,
			smooth = 0
	)

plots streamlines from a vector field.

Use density=[densx,densy] to control how close streamlines are allowed to get.

Definition at line 514 of file amrplot.py.

    '''plots streamlines from a vector field.  Use density=[densx,densy] to control how close streamlines are allowed to get.'''
    if fig==None:
        ax=plt.gca()
    else:
        ax=fig.ax

    xrange=[ax.get_xlim()[0],ax.get_xlim()[1]]
    yrange=[ax.get_ylim()[0],ax.get_ylim()[1]]

    if nmax == 600 and fig != None:
        nmax = fig.dpi * max([fig.fig_w,fig.fig_h])


    # Aspect ratio:
    r=(xrange[1]-xrange[0])/(yrange[1]-yrange[0])
    if r<1:
        ny=nmax
        nx=int(r*nmax)
    else:
        nx=nmax
        ny=int(nmax/r)
    nregrid = [nx,ny]
    print(nregrid)
    
    CC=d.getCenterPoints()
    tmp0=np.complex(0,nregrid[0])
    tmp1=np.complex(0,nregrid[1])
    x=np.linspace(xrange[0],xrange[1],nregrid[0])
    y=np.linspace(yrange[0],yrange[1],nregrid[1])
    grid_x, grid_y = np.mgrid[xrange[0]:xrange[1]:tmp0, yrange[0]:yrange[1]:tmp1]

    u = griddata(CC, u1, (grid_x, grid_y), method='linear')
    v = griddata(CC, u2, (grid_x, grid_y), method='linear')
    uisnan=np.isnan(u)
    visnan=np.isnan(v)
    un = np.empty(np.shape(u))
    vn = np.empty(np.shape(v))
    un[uisnan] = griddata(CC, u1, (grid_x[uisnan], grid_y[uisnan]), method='nearest')
    vn[visnan] = griddata(CC, u2, (grid_x[visnan], grid_y[visnan]), method='nearest')
    u[uisnan]=un[uisnan]
    v[visnan]=vn[visnan]

    if (smooth != 0):
        u = ndimage.gaussian_filter(u, sigma=smooth)
        v = ndimage.gaussian_filter(v, sigma=smooth)

    if (x0 != None and y0!= None):
        for myx in zip(x0,y0):
            streamplot(x, y, u.transpose(), v.transpose(), x_0=myx[0], 
                       y_0=myx[1], density=density, linewidth=linewidth,
                       INTEGRATOR='RK4', color=color, arrowsize=arrowsize,alpha=alpha)
    else:
        streamplot(x, y, u.transpose(), v.transpose(),  
                   density=density, linewidth=linewidth,
                   INTEGRATOR='RK4', color=color, arrowsize=arrowsize,alpha=alpha)

#=============================================================================

velovect()

def amrplot.velovect	(		u1,
			u2,
			d,
			nvect = None,
			scalevar = None,
			scale = 100,
			color = 'k',
			ax = None,
			alpha = 1.
	)

Plots normalized velocity vectors.

Definition at line 442 of file amrplot.py.

    '''Plots normalized velocity vectors'''

    minvel=1e-40

    if ax==None:
        ax=plt.gca()

    CC=d.getCenterPoints()
    n=np.sqrt(u1**2+u2**2)
    # remove zero velocity:
    m=n<minvel
    vr=np.ma.filled(np.ma.masked_array(u1/n,m),0.)
    vz=np.ma.filled(np.ma.masked_array(u2/n,m),0.)
    if scalevar is not None:
        vr = vr*scalevar
        vz = vz*scalevar
    if nvect==None:
        Q=ax.quiver(CC[:,0],CC[:,1],vr,vz,pivot='middle',width=1e-3,minlength=0.,scale=scale,
                    headwidth=6,alpha=alpha)
    else:
        # regrid the data:
        tmp0=np.complex(0,nvect[0])
        tmp1=np.complex(0,nvect[1])
        grid_r, grid_z = np.mgrid[ax.get_xlim()[0]:ax.get_xlim()[1]:tmp0, ax.get_ylim()[0]:ax.get_ylim()[1]:tmp1]
        grid_vr = griddata(CC, vr, (grid_r, grid_z), method='nearest')
        grid_vz = griddata(CC, vz, (grid_r, grid_z), method='nearest')
        Q=ax.quiver(grid_r,grid_z,grid_vr,grid_vz,pivot='middle',width=2e-3,minlength=minvel,scale=scale,
                    headwidth=10,headlength=10,color=color,edgecolor=color,rasterized=True,alpha=alpha)

    plt.draw()
    return Q     

#=============================================================================

Variable Documentation

default_timer

amrplot.default_timer = time.clock

Definition at line 19 of file amrplot.py.

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