vis Namespace Reference

Functions
def	delaunay (dataName, dataType, value)
def	voronoi (dataName, dataType, value)
def	laplacian (density, name, imgdpi)
def	struct_fact (density, name, imgdpi)
def	opPot (dataName, imgdpi)
def	hist_gen (name, value, num_bins)
def	image_gen (dataName, initValue, finalValue, increment, imgdpi)
def	image_gen_single (dataName, value, imgdpi, opmode)
def	vort_traj (name, imgdpi)
def	scaleAxis (data, dataName, label, value, imgdpi)
def	overlap (dataName, initValue, finalValue, increment)

Variables
	CPUs = os.environ['SLURM_JOB_CPUS_PER_NODE']
	prec
	c = ConfigParser.ConfigParser()
	xDim = int(c.getfloat('Params','xDim'))
	yDim = int(c.getfloat('Params','yDim'))
	gndMaxVal = int(c.getfloat('Params','gsteps'))
	evMaxVal = int(c.getfloat('Params','esteps'))
	incr = int(c.getfloat('Params','print_out'))
tuple	sep = (c.getfloat('Params','dx'))
tuple	dx = (c.getfloat('Params','dx'))
tuple	dt = (c.getfloat('Params','dt'))
tuple	xMax = (c.getfloat('Params','xMax'))
tuple	yMax = (c.getfloat('Params','yMax'))
int	num_vort = 0
	data = numpy.ndarray(shape=(xDim,yDim))
list	gndImgList = []
list	evImgList = []
list	gnd_proc = []
list	ev_proc = []
list	i = gndImgList.pop()
list	proc = gnd_proc + ev_proc
list	p = proc.pop()

Detailed Description

vis.py - GPUE: Split Operator based GPU solver for Nonlinear 
Schrodinger Equation, Copyright (C) 2011-2015, Lee J. O'Riordan 
<loriordan@gmail.com>, Tadhg Morgan, Neil Crowley. All rights reserved.

Redistribution and use in source and binary forms, with or without 
modification, are permitted provided that the following conditions are 
met:

1. Redistributions of source code must retain the above copyright 
notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright 
notice, this list of conditions and the following disclaimer in the 
documentation and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its 
contributors may be used to endorse or promote products derived from 
this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A 
PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT 
HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Function Documentation

delaunay()

def vis.delaunay	(		dataName,
			dataType,
			value
	)

Definition at line 76 of file vis.py.

def delaunay(dataName,dataType,value):
    v_arr=genfromtxt(dataName + str(value) + dataType,delimiter=',' )
    data = np.array([[row[0],row[1]] for row in v_arr])
    dln = sp.spatial.Delaunay(data)
    plt.triplot(data[:,0],data[:,1],dln.simplices.copy(),linewidth=0.5,color='b',marker='.')
    plt.xlim(300,700);plt.ylim(300,700);
    plt.savefig('delaun_' + str(value) + '.png',dpi=200)
    print 'Saved Delaunay @ t=' + str(value)

hist_gen()

def vis.hist_gen	(		name,
			value,
			num_bins
	)

Definition at line 131 of file vis.py.

def hist_gen(name,value,num_bins):
    v_arr=genfromtxt('vort_arr_' + str(value),delimiter=',' )
    H=[]
    count=0

    for i1 in range(0,v_arr.size/2):
        for i2 in range(i1,v_arr.size/2):
            H.append(m.sqrt( abs(v_arr[i1][0]*sep - v_arr[i2][0]*sep)**2  +  abs(v_arr[i1][1]*sep - v_arr[i2][1]*sep)**2 ))
            count = count + 1
    plt.title('Vortex lattice @ t=' + str(value*dt))
    plt.ticklabel_format(style='scientific')
    plt.ticklabel_format(style='scientific',axis='x', scilimits=(0,0))
    h = plt.hist(H, bins=num_bins)
    plt.savefig(name + "_" + str(value) + ".pdf")
    plt.close()

image_gen()

def vis.image_gen	(		dataName,
			initValue,
			finalValue,
			increment,
			imgdpi
	)

Definition at line 147 of file vis.py.

def image_gen(dataName, initValue, finalValue, increment,imgdpi):
    for i in range(initValue,finalValue,increment):
        if not os.path.exists(dataName+"r_"+str(i)+"_abspsi2.png"):
            real=open(dataName + '_' + str(i)).read().splitlines()
            img=open(dataName + 'i_' + str(i)).read().splitlines()
            a_r = numpy.asanyarray(real,dtype='f8') #64-bit double
            a_i = numpy.asanyarray(img,dtype='f8') #64-bit double
            a = a_r[:] + 1j*a_i[:]
            b = np.reshape(a,(xDim,yDim))
            f = plt.imshow(abs(b)**2)
            plt.jet()
            plt.gca().invert_yaxis()
            plt.savefig(dataName+"r_"+str(i)+"_abspsi2.png",dpi=imgdpi)
            plt.close()
            g = plt.imshow(np.angle(b))
            plt.gca().invert_yaxis()
            plt.savefig(dataName+"r_"+str(i)+"_phi.png",dpi=imgdpi)
            plt.close()
            f = plt.imshow(abs(np.fft.fftshift(np.fft.fft2(b)))**2)
            plt.gca().invert_yaxis()
            plt.jet()
            plt.savefig(dataName+"p_"+str(i)+"_abspsi2.png",dpi=imgdpi)
            plt.close()
            g = plt.imshow(np.angle(np.fft.fftshift(np.fft.fft2(b))))
            plt.gca().invert_yaxis()
            plt.savefig(dataName+"p_"+str(i)+"_phi.png",dpi=imgdpi)
            plt.close()
            print "Saved figure: " + str(i) + ".png"
            plt.close()
        else:
            print "File(s) " + str(i) +".png already exist."

image_gen_single()

def vis.image_gen_single	(		dataName,
			value,
			imgdpi,
			opmode
	)

Definition at line 179 of file vis.py.

References laplacian(), and struct_fact().

def image_gen_single(dataName, value, imgdpi,opmode):
    real=open(dataName + '_' + str(0)).read().splitlines()
    img=open(dataName + 'i_' + str(0)).read().splitlines()
    a1_r = numpy.asanyarray(real,dtype='f8') #128-bit complex
    a1_i = numpy.asanyarray(img,dtype='f8') #128-bit complex
    a1 = a1_r[:] + 1j*a1_i[:]
    b1 = np.reshape(a1,(xDim,yDim))

    if not os.path.exists(dataName+"r_"+str(value)+"_abspsi2.png"):
        real=open(dataName + '_' + str(value)).read().splitlines()
        img=open(dataName + 'i_' + str(value)).read().splitlines()
        a_r = numpy.asanyarray(real,dtype='f8') #128-bit complex
        a_i = numpy.asanyarray(img,dtype='f8') #128-bit complex
        a = a_r[:] + 1j*a_i[:]
        b = np.reshape(a,(xDim,yDim))
        m_val=np.max(np.abs(b)**2)
        #scaleAxis(b,dataName,"_abspsi2",value,imgdpi)
        if opmode & 0b100000 > 0:
#           fig, ax = plt.subplots()
#           #plt.rc('text',usetex=True)
#           #plt.rc('font',family='serif')
#           f = plt.imshow((abs(b)**2 - abs(b1)**2),cmap='gnuplot2',vmin=-6,vmax=6)
#           plt.title(r'$\left(\rho( r,t ) - \rho( r,t_0 )\right),t=$' + str(value*dt))
#           cbar = fig.colorbar(f)
#           plt.gca().set_xlabel('x '+ str((dx)))
#           plt.gca().set_ylabel('x '+ str(dx))
#           plt.gca().invert_yaxis()
#           plt.savefig(dataName+"r_"+str(value)+"_diffabspsi2.png",dpi=imgdpi)
#           plt.close()
#           #plt.rc('text',usetex=True)
#           #plt.rc('font',family='serif')

            fig, ax = plt.subplots()
            f = plt.imshow((abs(b)**2),cmap='gnuplot2',vmin=0,vmax=1e7)
            plt.title('rho(r) @ t=' + str(value*dt))
        #   plt.title(r'$\\rho \left( r,t \right),\,t=$' + str(value*dt))
            
            #plugins.connect(fig, plugins.MousePosition(fontsize=14))
            
            cbar = fig.colorbar(f)
            plt.gca().set_xlabel('x '+ str((dx)))
            plt.gca().set_ylabel('x '+ str(dx))
            plt.gca().invert_yaxis()
            plt.savefig(dataName+"r_"+str(value)+"_abspsi2.png",dpi=imgdpi)
            plt.axis('off')
            plt.savefig(dataName+"r_"+str(value)+"_abspsi2_axis0.pdf",bbox_inches='tight',dpi=imgdpi)
            plt.close()

        if opmode & 0b010000 > 0:
            fig, ax = plt.subplots()
            g = plt.imshow(np.angle(b))
            cbar = fig.colorbar(g)
            plt.gca().invert_yaxis()
            plt.title('theta(r) @ t=' + str(value*dt))
            plt.savefig(dataName+"r_"+str(value)+"_phi.png",dpi=imgdpi)
            plt.close()

        if opmode & 0b001000 > 0:
            fig, ax = plt.subplots()
            f = plt.imshow(abs(np.fft.fftshift(np.fft.fft2(b)))**2)
            cbar = fig.colorbar(f)
            plt.gca().invert_yaxis()
            plt.jet()
            plt.title('rho(p) @ t=' + str(value*dt))
            plt.savefig(dataName+"p_"+str(value)+"_abspsi2.png",dpi=imgdpi)
            plt.close()

        if opmode & 0b000100 > 0:
            fig, ax = plt.subplots()
            g = plt.imshow(np.angle(np.fft.fftshift(np.fft.fft2(b))))
            cbar = fig.colorbar(g)
            plt.gca().invert_yaxis()
            plt.title('theta(p) @ t=' + str(value*dt))
            plt.savefig(dataName+"p_"+str(value)+"_phi.png",dpi=imgdpi)
            plt.close()

        if opmode & 0b000010 > 0:
            struct_fact(abs(b)**2,dataName+"_" + str(value),imgdpi)

        if opmode & 0b000001 > 0:
            laplacian(abs(b)**2,dataName+"_" + str(value),imgdpi)

        print "Saved figure: " + str(value) + ".png"
        plt.close()
    else:
        print "File(s) " + str(value) +".png already exist."

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laplacian()

def vis.laplacian	(		density,
			name,
			imgdpi
	)

Definition at line 94 of file vis.py.

Referenced by image_gen_single().

def laplacian(density,name,imgdpi):
    gx,gy = np.gradient(density)
    g2x,gxgy = np.gradient(gx)
    gygx,g2y = np.gradient(gy)
    fig, ax = plt.subplots()
    #f = plt.quiver(gx,gy)
    f = plt.imshow((g2x**2 + g2y**2),cmap=plt.get_cmap('spectral'))
    cbar = fig.colorbar(f)
    plt.savefig(name + "_laplacian.png",dpi=imgdpi)
    plt.close()
    f = plt.imshow((gxgy - gygx),cmap=plt.get_cmap('spectral'))
    cbar = fig.colorbar(f)
    plt.savefig(name + "_dxdy.png",dpi=imgdpi)
    plt.close()

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opPot()

def vis.opPot	(		dataName,
			imgdpi
	)

Definition at line 119 of file vis.py.

Referenced by overlap().

def opPot(dataName,imgdpi):
    data = open(dataName).read().splitlines()
    a = numpy.asanyarray(data,dtype='f8')
    b = np.reshape(a,(xDim,yDim))
    fig, ax = plt.subplots()
    f = plt.imshow((b))
    plt.gca().invert_yaxis()
    cbar = fig.colorbar(f)
    plt.jet()
    plt.savefig(dataName + ".png",dpi=imgdpi)
    plt.close()

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overlap()

def vis.overlap	(		dataName,
			initValue,
			finalValue,
			increment
	)

Definition at line 307 of file vis.py.

References opPot().

def overlap(dataName, initValue, finalValue, increment):
    real=open(dataName + '_' + str(0)).read().splitlines()
    img=open(dataName + 'i_' + str(0)).read().splitlines()
    a_r = numpy.asanyarray(real,dtype='f8') #128-bit complex
    a_i = numpy.asanyarray(img,dtype='f8') #128-bit complex
    wfc0 = a_r[:] + 1j*a_i[:]
    for i in range(initValue,finalValue,increment):
        real=open(dataName + '_' + str(value)).read().splitlines()
        img=open(dataName + 'i_' + str(value)).read().splitlines()
        a_r = numpy.asanyarray(real,dtype='f8') #128-bit complex
        a_i = numpy.asanyarray(img,dtype='f8') #128-bit complex
        a = a_r[:] + 1j*a_i[:]
        b = np.dot(wfc0,a)
        print i, np.sum(b)

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scaleAxis()

def vis.scaleAxis	(		data,
			dataName,
			label,
			value,
			imgdpi
	)

Definition at line 296 of file vis.py.

def scaleAxis(data,dataName,label,value,imgdpi):
    fig, ax = plt.subplots()
    ax.xaxis.set_major_locator(ScaledLocator(dx=dx))
    ax.xaxis.set_major_formatter(ScaledLocator(dx=dx))
    f = plt.imshow(abs(data)**2)
    cbar = fig.colorbar(f)
    plt.gca().invert_yaxis()
    plt.jet()
    plt.savefig(dataName+"r_"+str(value)+"_"+label +".png",dpi=imgdpi)
    plt.close()

struct_fact()

def vis.struct_fact	(		density,
			name,
			imgdpi
	)

Definition at line 109 of file vis.py.

Referenced by image_gen_single().

def struct_fact(density,name,imgdpi):
    fig, ax = plt.subplots()
    #f = plt.quiver(gx,gy)
    f = plt.imshow((np.abs(np.fft.fftshift(np.fft.fft2(density)))),cmap=plt.get_cmap('prism'))
    cbar = fig.colorbar(f)
    cbar.set_clim(1e6,1e11)
    plt.jet()
    plt.savefig(name + "_struct_log10.png",dpi=imgdpi)
    plt.close()

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voronoi()

def vis.voronoi	(		dataName,
			dataType,
			value
	)

Definition at line 85 of file vis.py.

def voronoi(dataName,dataType,value):
    v_arr=genfromtxt(dataName + str(value) + dataType,delimiter=',' )
    data = [[row[0],row[1]] for row in v_arr]
    vor = Voronoi(data)
    voronoi_plot_2d(vor)
    plt.xlim(300,700);plt.ylim(300,700);
    plt.savefig('voronoi_' + str(value) + '.png',dpi=200)
    print 'Saved Voronoi @ t=' + str(value)

vort_traj()

def vis.vort_traj	(		name,
			imgdpi
	)

Definition at line 266 of file vis.py.

def vort_traj(name,imgdpi):
    evMaxVal_l = evMaxVal
    H=genfromtxt('vort_arr_0',delimiter=',' )
    count=0
    for i1 in range(incr,evMaxVal_l,incr):
        try:
            v_arr=genfromtxt('vort_lsq_' + str(i1) + '.csv',delimiter=',' )
            H=np.column_stack((H,v_arr))
        except:
            evMaxVal_l = i1
            break
    X=np.zeros((evMaxVal_l/incr),dtype=np.float64)
    Y=np.zeros((evMaxVal_l/incr),dtype=np.float64)
    H=np.reshape(H,([num_vort,2,evMaxVal_l/incr]),order='F')
    for i1 in range(0, num_vort):
        for i2 in range(0,evMaxVal_l/incr):
            X[i2]=(H[i1,0,i2]*dx) - xMax
            Y[i2]=(H[i1,1,i2]*dx) - yMax
        h = plt.plot(X,Y,color=(r.random(),r.random(),r.random(),0.85),linewidth=0.1)
    plt.axis('equal')
    plt.title('Vort(x,y) from t=0 to t='+str(evMaxVal_l*dt)+" s")

    plt.axis((-xMax/2.0, xMax/2.0, -yMax/2.0, yMax/2.0))
    plt.ticklabel_format(style='scientific')
    plt.ticklabel_format(style='scientific',axis='x', scilimits=(0,0))
    plt.ticklabel_format(style='scientific',axis='y', scilimits=(0,0))
    plt.savefig(name +".pdf")
    plt.close()
    print "Trajectories plotted."

Variable Documentation

c

vis.c = ConfigParser.ConfigParser()

Definition at line 57 of file vis.py.

CPUs

vis.CPUs = os.environ['SLURM_JOB_CPUS_PER_NODE']

Definition at line 34 of file vis.py.

data

vis.data = numpy.ndarray(shape=(xDim,yDim))

Definition at line 74 of file vis.py.

dt

tuple vis.dt = (c.getfloat('Params','dt'))

Definition at line 69 of file vis.py.

dx

tuple vis.dx = (c.getfloat('Params','dx'))

Definition at line 68 of file vis.py.

ev_proc

list vis.ev_proc = []

Definition at line 333 of file vis.py.

evImgList

list vis.evImgList = []

Definition at line 327 of file vis.py.

evMaxVal

vis.evMaxVal = int(c.getfloat('Params','esteps'))

Definition at line 65 of file vis.py.

gnd_proc

list vis.gnd_proc = []

Definition at line 332 of file vis.py.

gndImgList

list vis.gndImgList = []

Definition at line 326 of file vis.py.

gndMaxVal

vis.gndMaxVal = int(c.getfloat('Params','gsteps'))

Definition at line 64 of file vis.py.

i

list vis.i = gndImgList.pop()

Definition at line 335 of file vis.py.

incr

vis.incr = int(c.getfloat('Params','print_out'))

Definition at line 66 of file vis.py.

num_vort

int vis.num_vort = 0

Definition at line 72 of file vis.py.

p

list vis.p = proc.pop()

Definition at line 347 of file vis.py.

prec

vis.prec

Definition at line 56 of file vis.py.

proc

list vis.proc = gnd_proc + ev_proc

Definition at line 341 of file vis.py.

sep

tuple vis.sep = (c.getfloat('Params','dx'))

Definition at line 67 of file vis.py.

xDim

vis.xDim = int(c.getfloat('Params','xDim'))

Definition at line 62 of file vis.py.

xMax

tuple vis.xMax = (c.getfloat('Params','xMax'))

Definition at line 70 of file vis.py.

yDim

vis.yDim = int(c.getfloat('Params','yDim'))

Definition at line 63 of file vis.py.

yMax

tuple vis.yMax = (c.getfloat('Params','yMax'))

Definition at line 71 of file vis.py.