mpi_vis.py

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'''
vis.py - GPUE: Split Operator based GPU solver for Nonlinear
Schrodinger Equation, Copyright (C) 2011-2018, 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.
'''
import os
from mpi4py import MPI
CPUs = 1#os.environ['SLURM_JOB_CPUS_PER_NODE']
from numpy import genfromtxt
import math as m
import matplotlib as mpl
import matplotlib.tri as tri
import numpy as np
import scipy as sp
from scipy.spatial import Voronoi, voronoi_plot_2d
import numpy.matlib
mpl.use('Agg')
from matplotlib.ticker import ScalarFormatter
import matplotlib.pyplot as plt
from matplotlib import pyplot as plt
import matplotlib.patheffects as PathEffects
import ConfigParser
import random as r
from decimal import *
import stats
import hist3d

getcontext().prec = 4
c = ConfigParser.ConfigParser()
getcontext().prec = 4
c = ConfigParser.ConfigParser()
c.readfp(open(r'Params.dat'))

#Default value for wavefunction density max; modified by initially loaded wavefunction set, 
# and used for all subsequent plotting.
maxDensityVal=5e7 

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

def image_gen_single(dataName, value, imgdpi,opmode, x_dat, cbarOn=True, plot_vtx=False):
    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.transpose(np.reshape(a,(xDim,yDim))) #Transpose to match matlab plots
        if value=0:
            maxDensityVal=np.max(np.abs(a)**2)
        startBit = 0x00

        try:
            vorts = np.loadtxt('vort_ord_' + str(value) + '.csv', delimiter=',', unpack=True)
        except Exception as e:
            print "Failed to load the required data file: %s"%e
            print "Please run vort.py before plotting the density, if you wih to have correctly numbered vortices"
            vorts = np.loadtxt('vort_arr_' + str(value), delimiter=',', unpack=True, skiprows=1)
            startBit=0x01

        if opmode & 0b100000 > 0:
            nameStr = dataName+"r_"+str(value)

            fig, ax = plt.subplots()
            f = plt.imshow( (abs(b)**2), cmap='hot', vmin=0, vmax=maxDensityVal, interpolation='none',)
                                #extent=[-xMax, xMax, -xMax, xMax])
            tstr =  str(value*dt)
            plt.title('t=' + tstr + " s", fontsize=28)

            if cbarOn==True:
                tbar = fig.colorbar(f)

            plt.gca().invert_yaxis()
            if plot_vtx==True:
                if startBit==0x00:
                    zVort = zip(vorts[0,:],vorts[1,:], vorts[3,:])
                else:
                    zVort = zip(vorts[1,:], vorts[3,:], [0, 1, 2, 3])
                for x, y, z in zVort:
                    if z==0:
                        txt = plt.text(x, y, str(int(z)), color='#379696', fontsize=6, alpha=0.7)
                        txt.set_path_effects([PathEffects.withStroke(linewidth=1, foreground='#B9EA56')])
                    else:
                        txt = plt.text(x, y, str(int(z)), color='#B9EA56', fontsize=6, alpha=0.7)
                        txt.set_path_effects([PathEffects.withStroke(linewidth=1, foreground='#379696')])
            plt.axis('equal')
            plt.axis('off')
            if plot_vtx==True:
                plt.savefig(dataName+"r_"+str(value)+"_abspsi2_num.png",dpi=imgdpi, bbox_inches='tight')
            else:
                plt.savefig(dataName+"r_"+str(value)+"_abspsi2_nonum.png",dpi=imgdpi, bbox_inches='tight')
            plt.close()

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

if __name__ == '__main__':
    import sys
    cbarOn = sys.argv[1].lower() == 'true'
    plot_vtx = sys.argv[2].lower() == 'true'
    gndImgList=[]
    evImgList=[]
    x_coord = np.loadtxt('x_0', unpack=True)

    comm = MPI.COMM_WORLD
    size = comm.size
    rank = comm.rank
    print "Rank %d/%d initialised"%(rank,size-1)

    arrG = np.array_split( xrange(0,gndMaxVal,incr), size)
    arrE = np.array_split( xrange(0,evMaxVal,incr), size)

    for i in arrG[rank]:
        gndImgList.append(i)
    for i in arrE[rank]:
        evImgList.append(i)

    while gndImgList:

        print "Processing data index=%d on rank=%d"%(i,rank)
        i=gndImgList.pop()

        try:
            image_gen_single("wfc_0_ramp", i, 400, 0b100000, x_coord, cbarOn, plot_vtx)
        except Exception as e:
            print "Unable to process wfc_0_ramp data index=%d on rank=%d"%(i,rank)            
        try:
            image_gen_single("wfc_0_const", i, 400, 0b100000, x_coord, cbarOn, plot_vtx)
        except Exception as e:
            print "Unable to process wfc_0_const data index=%d on rank=%d"%(i,rank)

    while evImgList:
        print "Processing data index=%d on rank=%d"%(i,rank)
        i=evImgList.pop()
        try:
            image_gen_single("wfc_ev", i, 400, 0b100000, x_coord, cbarOn, plot_vtx)
            print "Processed data index=%d on rank=%d"%(i,rank)

        except Exception as e:
            print "Unable to process wfc_ev data index=%d on rank=%d"%(i,rank)