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Merge branch 'master' of https://github.com/mlxd/gpue
Updated readme file merge.
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| ''' | ||
| en.py - GPUE: Split Operator based GPU solver for Nonlinear | ||
| Schrodinger Equation, Copyright (C) 2011-2015, Lee J. O'Riordan | ||
| <[email protected]>, 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. | ||
| ''' | ||
| import os | ||
| CPUs = os.environ['SLURM_JOB_CPUS_PER_NODE'] | ||
| print "Number of cores: " + str(CPUs) | ||
| from numpy import genfromtxt | ||
| import math as m | ||
| import matplotlib as mpl | ||
| import numpy as np | ||
| import scipy as sp | ||
| import numpy.matlib | ||
| mpl.use('Agg') | ||
| import ConfigParser | ||
| import random as r | ||
| from decimal import * | ||
|
|
||
| getcontext().prec = 4 | ||
| c = ConfigParser.ConfigParser() | ||
| getcontext().prec = 4 | ||
| c = ConfigParser.ConfigParser() | ||
| c.readfp(open(r'Params.dat')) | ||
|
|
||
| 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')) | ||
| sep = (c.getfloat('Params','dx')) | ||
| dx = (c.getfloat('Params','dx')) | ||
| dy = (c.getfloat('Params','dx')) | ||
| dt = (c.getfloat('Params','dt')) | ||
| xMax = (c.getfloat('Params','xMax')) | ||
| yMax = (c.getfloat('Params','yMax')) | ||
| num_vort = 0#int(c.getfloat('Params','Num_vort')) | ||
|
|
||
| data = numpy.ndarray(shape=(xDim,yDim)) | ||
| K = np.reshape(np.array(open('K_0').read().splitlines(),dtype='f8'),(xDim,yDim)) | ||
| V = np.reshape(np.array(open('V_0').read().splitlines(),dtype='f8'),(xDim,yDim)) | ||
| X = np.array(open('x_0').read().splitlines(),dtype='f8') | ||
| Y = np.array(open('y_0').read().splitlines(),dtype='f8') | ||
| XM,YM = np.meshgrid(X,Y) | ||
| R = (XM**2+YM**2) | ||
| macheps = 7./3. - 4./3. - 1. #http://rstudio-pubs-static.s3.amazonaws.com/13303_daf1916bee714161ac78d3318de808a9.html | ||
| Q = (XM**2-YM**2) | ||
|
|
||
| def expectValueR(dataName,i,Val): | ||
| 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)) | ||
| return np.real(np.trapz(np.trapz(np.conj(wfcr)*Val*wfcr))*dx*dy) | ||
|
|
||
| def energy_total(dataName,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*wfcr | ||
|
|
||
| E_k = np.trapz(np.trapz(wfcr_c*E1))*dx*dy | ||
| E_vi = np.trapz(np.trapz(wfcr_c*E2))*dx*dy | ||
| return np.real(E_k + E_vi) | ||
|
|
||
| #for ii in range(0,gndMaxVal,incr): | ||
| # print "E(t={} s)={}".format(ii*dt,energy_total('wfc_0_const',ii)) | ||
|
|
||
| for ii in range(0,evMaxVal,incr): | ||
| # print "E(t={} s)={}".format(ii*dt,energy_total('wfc_ev',ii)) | ||
| #print "R(t={} s)={}".format(ii*dt,rad('wfc_ev',ii)) | ||
| print "{},{}".format(ii*dt,expectValueR('wfc_ev',ii,R)) | ||
|
|