Functions
def	kinertrum (Psi, dx, i, quOn)
	Kinetic energy spectrum = kinertrum. More...

def	kinertrum_loop (dataName, initValue, finalValue, incr)

def	dens_struct_fact (dataName, initValue, finalValue, incr)

def	energy_total (dataName, initValue, finalValue, increment)

def	energy_kinetic (dataName, initValue, finalValue, increment)

def	energy_potential (dataName, initValue, finalValue, increment)

def	ang_mom (dataName, initValue, finalValue, incr, ev_type, imgdpi)

def	expec_val_monopole (dataName, initValue, finalValue, incr)

def	expec_val_quadrupole (dataName, initValue, finalValue, incr)

def	expec_val_ (quant_name, quantity, dataName, initValue, finalValue, incr)

Variables
float	HBAR = 1.05457148e-34

float	PI = 3.141592653589793

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

tuple	dy = (c.getfloat('Params','dy'))

tuple	dkx = (c.getfloat('Params','dpx'))

tuple	dky = (c.getfloat('Params','dpy'))

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

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

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

tuple	omegaZ = (c.getfloat('Params','omegaZ'))

tuple	mass = (c.getfloat('Params','Mass'))

tuple	omega = (c.getfloat('Params','omega'))

tuple	omegaX = (c.getfloat('Params','omegaX'))

	num_vort = int(c.getfloat('Params','Num_vort'))

	N = int(c.getfloat('Params','atoms'))

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

	x = np.asarray(open('x_0').read().splitlines(),dtype='f8')

	y = np.asarray(open('y_0').read().splitlines(),dtype='f8')

	kx = np.reshape( np.array( [np.linspace( 0, (xDim/2-1)dkx, xDim/2), np.linspace( (-xDim/2-1)dkx, -dkx, xDim/2)]), (xDim,1) )

	ky = np.reshape( np.array( [np.linspace( 0, (yDim/2-1)dky, yDim/2), np.linspace( (-yDim/2-1)dky, -dky, yDim/2)]), (yDim,1) )

	kxm

	kym

	km_mag = np.sqrt( kxm2 + kym2 )

	k_mag = np.sqrt( kx2 + ky2 )

	kMax = max(max(k_mag))

float	hbar = 1.05457e-34

float	m = 1.4431607e-25

	V = np.array(open('V_0').read().splitlines(),dtype='f8')

	K = np.array(open('K_0').read().splitlines(),dtype='f8')

	xPy = np.array(open('xPy_0').read().splitlines(),dtype='f8')

	yPx = np.array(open('yPx_0').read().splitlines(),dtype='f8')

tuple	g = (0.5N)4.0HBARHBARPI(4.67e-9/mass)np.sqrt(massomegaZ/(2.0PIHBAR))

Detailed Description

observables.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

◆ ang_mom()

def observables.ang_mom	(	dataName,
		initValue,
		finalValue,
		incr,
		ev_type,
		imgdpi
	)

Definition at line 293 of file observables.py.

Referenced by expec_val_().

 def ang_mom(dataName, initValue, finalValue, incr, ev_type, imgdpi):
     xm, ym = np.meshgrid(x,y)
     pxm, pym = np.meshgrid(px,py)
     dx2=dx**2
     Lz = np.zeros( (finalValue/incr))
     for i in range(initValue,incr*(finalValue/incr),incr):
         if os.path.exists(dataName + '_' + str(i)):
             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[:]
             wfc = np.reshape(a,(xDim,yDim))
             conjwfc = np.conj(wfc)
 
             wfc_ypx = np.multiply(ym,np.fft.ifft(np.multiply(pxm,np.fft.fft(wfc,axis=1)),axis=1))
             wfc_xpy = np.multiply(xm,np.fft.ifft(np.multiply(pym,np.fft.fft(wfc,axis=0)),axis=0))
             result = np.sum( np.sum( np.multiply(conjwfc,wfc_xpy - wfc_ypx) ) )*dx2
         else:
             print "Skipped " + dataName + "_"+ str(i)
             result = np.nan
 
         print i, incr
         Lz[(i/incr)] = np.real(result)
     type=""
     if ev_type == 0:
         type = "gnd"
     else:
         type = "ev"
     np.savetxt('Lz.csv',Lz,delimiter=',')
 
     plt.plot(Lz)
     plt.savefig("Lz_"+type+".pdf",dpi=imgdpi)
     plt.axis('off')
     plt.savefig("Lz_"+type+"_axis0.pdf",bbox_inches='tight',dpi=imgdpi)
     plt.close()
 

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◆ dens_struct_fact()

def observables.dens_struct_fact	(	dataName,
		initValue,
		finalValue,
		incr
	)

Definition at line 194 of file observables.py.

References kinertrum().

Referenced by expec_val_().

 def dens_struct_fact(dataName, initValue, finalValue,incr):
     n_k=np.zeros(finalValue/incr)
     n_k_t=np.zeros((finalValue/incr,xDim,yDim),dtype=np.complex128)
     for i in range(initValue,incr*(finalValue/incr),incr):
         if os.path.exists(dataName + '_' + str(i)):
             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[:]
             n = np.abs(a)**2
 
             kinertrum(np.reshape(a,(xDim,yDim)),dx,i,0)
             sf = np.fft.fftshift(np.fft.fft2(np.reshape(n,(xDim,yDim))))
             n_k_t[i/incr][:][:] = sf[:][:];
             n_k[i/incr]=(abs(np.sum(np.sum(sf))*dkx**2))
 
             fig, ax = plt.subplots()
             f = plt.imshow(np.log10(abs(sf)),cmap=plt.get_cmap('gnuplot2'))
             cbar = fig.colorbar(f)
             plt.gca().invert_yaxis()
             plt.savefig("struct_" + str(i/incr) + ".png",vmin=0,vmax=12,dpi=200)
             plt.close()
             print i/incr
 
     np.savetxt('Struct' + '.csv',n_k,delimiter=',')
     plt.plot(range(initValue,finalValue,incr),n_k)
     sp.io.savemat('Struct_t.mat',mdict={'n_k_t',n_k_t})
     plt.savefig("Struct.pdf",dpi=200)
     plt.close()
 

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◆ energy_kinetic()

def observables.energy_kinetic	(	dataName,
		initValue,
		finalValue,
		increment
	)

Definition at line 268 of file observables.py.

Referenced by expec_val_().

 def energy_kinetic(dataName, initValue, finalValue, increment):
     px1 = np.fft.fftshift(px)
     py1 = np.fft.fftshift(py)
     dk=[]
     dk2[:] = (px1[:]**2 + py1[:]**2)
     Lz = np.zeros( (finalValue/incr))
     for i in range(initValue,incr*(finalValue/incr),incr):
         if os.path.exists(dataName + '_' + str(i)):
             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[:]
             wfcp = np.fft.fft2(np.reshape(a,(xDim,yDim)))
             conjwfcp = np.conj(wfcp)
             E_k = np.zeros(len(px1))
             for ii in range(0,len(px1)):
                 E_k[ii] = np.sum( np.sum( np.multiply(wfcp,conjwfcp) )  )*dk2[ii]
 
         np.savetxt('E_k_' + str(i) + '.csv',E_k,delimiter=',')
         print i
 

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◆ energy_potential()

def observables.energy_potential	(	dataName,
		initValue,
		finalValue,
		increment
	)

Definition at line 290 of file observables.py.

 def energy_potential(dataName, initValue, finalValue, increment):
     print 'energy'
 

◆ energy_total()

def observables.energy_total	(	dataName,
		initValue,
		finalValue,
		increment
	)

Definition at line 235 of file observables.py.

Referenced by expec_val_().

 def energy_total(dataName, initValue, finalValue, increment):
     E=np.zeros((finalValue,1))
     E_k=np.zeros((finalValue,1))
     E_vi=np.zeros((finalValue,1))
     E_l=np.zeros((finalValue,1))
     for i in range(initValue,incr*(finalValue/incr),incr):
         if os.path.exists(dataName + '_' + str(i)):
             real=open(dataName + '_' + str(i)).read().splitlines()
             img=open(dataName + 'i_' + str(i)).read().splitlines()
             a_r = np.array(real,dtype='f8') #64-bit double
             a_i = np.array(img,dtype='f8') #64-bit double
             wfcr = np.reshape(a_r[:] + 1j*a_i[:],(xDim,yDim))
             wfcp = np.array(np.fft.fft2(wfcr))
             wfcr_c = np.conj(wfcr)
             
             E1 = np.fft.ifft2(K*wfcp)
             E2 = (V + 0.5*g*np.abs(wfcr)**2)*wfcr
             E3 = -(omega*omegaX)*(np.fft.ifft(xPy*np.fft.fft(wfcr,axis=0),axis=0) - np.fft.ifft(yPx*np.fft.fft(wfcr,axis=1),axis=1)  )
             
             E_k[i/incr] = np.trapz(np.trapz(wfcr_c*E1))*dx*dy
             E_vi[i/incr] = np.trapz(np.trapz(wfcr_c*E2))*dx*dy
             E_l[i/incr] = np.trapz(np.trapz(wfcr_c*E3))*dx*dy
             E[i/incr] = E_k[i/incr] + E_vi[i/incr] + E_l[i/incr]
             print (i/float(evMaxVal))
     np.savetxt('E_'+ str(i) + '.csv',E,delimiter=',')
     np.savetxt('E_k_'+ str(i) + '.csv',E_k,delimiter=',')
     np.savetxt('E_vi_'+ str(i) + '.csv',E_vi,delimiter=',')
     np.savetxt('E_l_'+ str(i) + '.csv',E_l,delimiter=',')
     t = np.array(range(initValue,finalValue,incr))/dt
     plt.plot(t,E,'r-',t,E_k,'g-',t,E_vi,'b-',t,E_l,'y-')
     plt.savefig("EnergyVst.pdf",dpi=200)
     plt.close()
 

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◆ expec_val_()

def observables.expec_val_	(	quant_name,
		quantity,
		dataName,
		initValue,
		finalValue,
		incr
	)

Definition at line 382 of file observables.py.

References ang_mom(), dens_struct_fact(), energy_kinetic(), energy_total(), expec_val_monopole(), expec_val_quadrupole(), and kinertrum_loop().

 def expec_val_(quant_name, quantity, dataName, initValue, finalValue, incr):
     x=np.asarray(open('x_0').read().splitlines(),dtype='f8')
     y=np.asarray(open('y_0').read().splitlines(),dtype='f8')
     xm, ym = np.meshgrid(x, y)
     result = []
     for i in range(initValue,finalValue,incr):
         if not os.path.exists(dataName):
             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[:]
             wfc = np.reshape(a,(xDim,yDim))
             conjwfc = np.conj(wfc)
 
             d1 = np.multiply( quantity, wfc )
             d2 = np.multiply( conjwfc, d1)
             result.append( np.real( np.sum( np.sum( d2  ) ) )*dx*dx )
         print str(100*float(i)/finalValue) + '%'
     np.savetxt(quant_name + '.csv',result,delimiter=',')
     plt.plot(range(initValue,finalValue,incr),result)
     plt.savefig(quant_name + ".pdf",dpi=200)
     plt.close()
 

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◆ expec_val_monopole()

def observables.expec_val_monopole	(	dataName,
		initValue,
		finalValue,
		incr
	)

Definition at line 330 of file observables.py.

Referenced by expec_val_().

 def expec_val_monopole(dataName, initValue, finalValue, incr):
     x=np.asarray(open('x_0').read().splitlines(),dtype='f8')
     y=np.asarray(open('y_0').read().splitlines(),dtype='f8')
 #   px=open('px_0')
 #   py=open('py_0')
     xm, ym = np.meshgrid(x, y)
     result = []
     for i in range(initValue,finalValue,incr):
         if not os.path.exists(dataName):
             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[:]
             wfc = np.reshape(a,(xDim,yDim))
             conjwfc = np.conj(wfc)
 
             d1 = np.multiply( np.square(xm) + np.square(ym), wfc )
             d2 = np.multiply( conjwfc, d1)
             result.append( np.real( np.sum( np.sum( d2  ) ) )*dx*dx )
         print str(100*float(i)/finalValue) + '%'
     np.savetxt('monopole.csv',result,delimiter=',')
     plt.plot(range(initValue,finalValue,incr),result)
     plt.savefig("Monopole.png",dpi=200)
     plt.close()
 

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◆ expec_val_quadrupole()

def observables.expec_val_quadrupole	(	dataName,
		initValue,
		finalValue,
		incr
	)

Definition at line 356 of file observables.py.

Referenced by expec_val_().

 def expec_val_quadrupole(dataName, initValue, finalValue, incr):
     x=np.asarray(open('x_0').read().splitlines(),dtype='f8')
     y=np.asarray(open('y_0').read().splitlines(),dtype='f8')
 #   px=open('px_0')
 #   py=open('py_0')
     xm, ym = np.meshgrid(x, y)
     result = []
     for i in range(initValue,finalValue,incr):
         if not os.path.exists(dataName):
             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[:]
             wfc = np.reshape(a,(xDim,yDim))
             conjwfc = np.conj(wfc)
 
             d1 = np.multiply( np.square(xm) - np.square(ym), wfc )
             d2 = np.multiply( conjwfc, d1)
             result.append( np.real( np.sum( np.sum( d2  ) ) )*dx*dx )
         print str(100*float(i)/finalValue) + '%'
     np.savetxt('quadrupole.csv',result,delimiter=',')
     plt.plot(range(initValue,finalValue,incr),result)
     plt.savefig("Quadrupole.png",dpi=200)
     plt.close()
 

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◆ kinertrum()

def observables.kinertrum	(	Psi,
		dx,
		i,
		quOn
	)

Kinetic energy spectrum = kinertrum.

Calculates the spectrum for compressible and incompressible kinetic energies.

Parameters

Psi	The wavefunction
dx	Increment along x
i	The current step number
quOn	Boolean to turn on quantum kinetic energy spectrum (includes phase term).

Definition at line 104 of file observables.py.

Referenced by dens_struct_fact(), and kinertrum_loop().

 def kinertrum(Psi, dx, i, quOn):
 
     kMax = np.max(np.max(kx))
     Psi[np.where(Psi==0)] = 1e-100
     n_r = np.abs(Psi)**2
     n_r[np.where(n_r==0)] = 1e-100
     cPsi = np.conj(Psi)
     phi = np.angle(Psi)
 
     ph1 = np.unwrap(phi, axis=0)
     ph2 = np.unwrap(phi, axis=1)
 
     vel_ph1_x, vel_ph1_y = np.gradient(ph1,dx,dy)
     vel_ph2_x, vel_ph2_y = np.gradient(ph2,dx,dy)
 
     v_x = (hbar/m)*vel_ph1_x;
     v_y = (hbar/m)*vel_ph2_y;
     v_x[np.where(v_x==0)] = 1e-100
     v_y[np.where(v_y==0)] = 1e-100
 
     u_x = np.multiply(np.abs(Psi),v_x)
     u_y = np.multiply(np.abs(Psi),v_y)
 
     if quOn:
         u_x = np.multiply(u_x,np.exp(1j*np.angle(Psi)))
         u_y = np.multiply(u_y,np.exp(1j*np.angle(Psi)))
 
     u_kx = np.fft.fftn(u_x)
     u_ky = np.fft.fftn(u_y)
 
     uc_kx = ( kxm**2*u_kx + kxm*kym*u_ky ) / ( km_mag**2 + 1e-100 )
     uc_ky = ( kym*kxm*u_kx + kym**2*u_ky ) / ( km_mag**2 + 1e-100 )
 
     ui_kx = u_kx - uc_kx
     ui_ky = u_ky - uc_ky
 
     uc_x = np.fft.ifftn(uc_kx)
     uc_y = np.fft.ifftn(uc_ky)
     ui_x = np.fft.ifftn(ui_kx)
     ui_y = np.fft.ifftn(ui_ky)
 
     Ec = 0.5*np.abs(np.square(uc_x) + np.square(uc_y))
     Ei = 0.5*np.abs(np.square(ui_x) + np.square(ui_y))
 
     fig, ax = plt.subplots()
     f = plt.imshow((Ec),cmap=plt.get_cmap('gnuplot2'))
     cbar = fig.colorbar(f)
     plt.gca().invert_yaxis()
     plt.savefig("Ec_" + str(i/incr) + ".png",dpi=200)
     plt.close()
     fig, ax = plt.subplots()
     f = plt.imshow((Ei),cmap=plt.get_cmap('gnuplot2'))
     cbar = fig.colorbar(f)
     plt.gca().invert_yaxis()
     plt.savefig("Ei_" + str(i/incr) + ".png",dpi=200)
     plt.close()
     
     print Ec
     #exit()
     ekc = np.zeros((xDim/2-1,1))
     eki = np.zeros((xDim/2-1,1))
     for i1 in np.arange(0,np.size(k_mag)/2 -2):
         iX = np.array(np.where(np.logical_and( k_mag[i1] >= km_mag, k_mag[i1+1] < km_mag )))
 #        Ei_kx = np.sum(np.sum(np.abs(ui_kx[iX]**2*k[iX]))
 #        Ei_ky = np.sum(np.sum(np.abs(ui_ky[iX]**2*k[iX]))
         ekc[i1] = (0.5*m*k_mag[i1]) * (np.sum(np.abs(uc_kx[iX]**2 + uc_ky[iX]**2)))/len(iX)
         eki[i1] = (0.5*m*k_mag[i1]) * (np.sum(np.abs(ui_kx[iX]**2 + ui_ky[iX]**2)))/len(iX)
     print i1
     np.savetxt('ekc_' + str(i) + '.csv',ekc,delimiter=',')
     np.savetxt('eki_' + str(i) + '.csv',eki,delimiter=',')
     fig, ax = plt.subplots()
     print eki[0:(xDim/2-2)]
     f = plt.loglog(np.ravel(k_mag[0:(xDim/2 -2)]),eki[0:(xDim/2-2)])
     plt.savefig("eki_" + str(i) + ".png",dpi=200)
     f = plt.loglog(np.ravel(k_mag[0:(xDim/2 -2)]),np.ravel(ekc[0:(xDim/2-2)]))
     plt.savefig("ekc_" + str(i) + ".png",dpi=200)
     plt.close()
 
 

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◆ kinertrum_loop()

def observables.kinertrum_loop	(	dataName,
		initValue,
		finalValue,
		incr
	)

Definition at line 183 of file observables.py.

References kinertrum().

Referenced by expec_val_().

 def kinertrum_loop(dataName, initValue, finalValue, incr):
     for i in range(initValue,incr*(finalValue/incr),incr):
         if os.path.exists(dataName + '_' + str(i)):
             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[:]
 
             kinertrum(np.reshape(a,(xDim,yDim)),dx,i,1)
 

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Variable Documentation

◆ c

observables.c = ConfigParser.ConfigParser()

Definition at line 55 of file observables.py.

◆ data

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

Definition at line 82 of file observables.py.

◆ dkx

tuple observables.dkx = (c.getfloat('Params','dpx'))

Definition at line 66 of file observables.py.

◆ dky

tuple observables.dky = (c.getfloat('Params','dpy'))

Definition at line 67 of file observables.py.

◆ dt

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

Definition at line 68 of file observables.py.

◆ dx

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

Definition at line 64 of file observables.py.

◆ dy

tuple observables.dy = (c.getfloat('Params','dy'))

Definition at line 65 of file observables.py.

◆ evMaxVal

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

Definition at line 61 of file observables.py.

◆ g

tuple observables.g = (0.5*N)*4.0*HBAR*HBAR*PI*(4.67e-9/mass)*np.sqrt(mass*omegaZ/(2.0*PI*HBAR))

Definition at line 233 of file observables.py.

◆ gndMaxVal

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

Definition at line 60 of file observables.py.

◆ HBAR

float observables.HBAR = 1.05457148e-34

Definition at line 51 of file observables.py.

◆ hbar

float observables.hbar = 1.05457e-34

Definition at line 96 of file observables.py.

◆ incr

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

Definition at line 62 of file observables.py.

◆ K

observables.K = np.array(open('K_0').read().splitlines(),dtype='f8')

Definition at line 227 of file observables.py.

◆ k_mag

observables.k_mag = np.sqrt( kx**2 + ky**2 )

Definition at line 93 of file observables.py.

◆ km_mag

observables.km_mag = np.sqrt( kxm**2 + kym**2 )

Definition at line 92 of file observables.py.

◆ kMax

observables.kMax = max(max(k_mag))

Definition at line 94 of file observables.py.

◆ kx

observables.kx = np.reshape( np.array( [np.linspace( 0, (xDim/2-1)*dkx, xDim/2), np.linspace( (-xDim/2-1)*dkx, -dkx, xDim/2)]), (xDim,1) )

Definition at line 89 of file observables.py.

◆ kxm

observables.kxm

Definition at line 91 of file observables.py.

◆ ky

observables.ky = np.reshape( np.array( [np.linspace( 0, (yDim/2-1)*dky, yDim/2), np.linspace( (-yDim/2-1)*dky, -dky, yDim/2)]), (yDim,1) )

Definition at line 90 of file observables.py.

◆ kym

observables.kym

Definition at line 91 of file observables.py.

◆ m

float observables.m = 1.4431607e-25

Definition at line 97 of file observables.py.

◆ mass

tuple observables.mass = (c.getfloat('Params','Mass'))

Definition at line 72 of file observables.py.

◆ N

observables.N = int(c.getfloat('Params','atoms'))

Definition at line 80 of file observables.py.

◆ num_vort

observables.num_vort = int(c.getfloat('Params','Num_vort'))

Definition at line 77 of file observables.py.

◆ omega

tuple observables.omega = (c.getfloat('Params','omega'))

Definition at line 73 of file observables.py.

◆ omegaX

tuple observables.omegaX = (c.getfloat('Params','omegaX'))

Definition at line 74 of file observables.py.

◆ omegaZ

tuple observables.omegaZ = (c.getfloat('Params','omegaZ'))

Definition at line 71 of file observables.py.

◆ PI

float observables.PI = 3.141592653589793

Definition at line 52 of file observables.py.

◆ prec

observables.prec

Definition at line 54 of file observables.py.

◆ V

observables.V = np.array(open('V_0').read().splitlines(),dtype='f8')

Definition at line 225 of file observables.py.

◆ x

observables.x = np.asarray(open('x_0').read().splitlines(),dtype='f8')

Definition at line 84 of file observables.py.

Referenced by latexFig().

◆ xDim

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

Definition at line 58 of file observables.py.

◆ xMax

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

Definition at line 69 of file observables.py.

◆ xPy

observables.xPy = np.array(open('xPy_0').read().splitlines(),dtype='f8')

Definition at line 229 of file observables.py.

◆ y

observables.y = np.asarray(open('y_0').read().splitlines(),dtype='f8')

Definition at line 85 of file observables.py.

Referenced by latexFig().

◆ yDim

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

Definition at line 59 of file observables.py.

◆ yMax

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

Definition at line 70 of file observables.py.

◆ yPx

observables.yPx = np.array(open('yPx_0').read().splitlines(),dtype='f8')

Definition at line 231 of file observables.py.

Functions

Variables

Detailed Description

Function Documentation

◆ ang_mom()

◆ dens_struct_fact()

◆ energy_kinetic()

◆ energy_potential()

◆ energy_total()

◆ expec_val_()

◆ expec_val_monopole()

◆ expec_val_quadrupole()

◆ kinertrum()

◆ kinertrum_loop()

Variable Documentation

◆ c

◆ data

◆ dkx

◆ dky

◆ dt

◆ dx

◆ dy

◆ evMaxVal

◆ g

◆ gndMaxVal

◆ HBAR

◆ hbar

◆ incr

◆ K

◆ k_mag

◆ km_mag

◆ kMax

◆ kx

◆ kxm

◆ ky

◆ kym

◆ m

◆ mass

◆ N

◆ num_vort

◆ omega

◆ omegaX

◆ omegaZ

◆ PI

◆ prec

◆ V

◆ x

◆ xDim

◆ xMax

◆ xPy

◆ y

◆ yDim

◆ yMax

◆ yPx