Matplotlib setting background color and vectorizing FDTD equation

Question

I'm currently having some trouble with Matplotlib. I have an FDTD program that gets 'washed out' as it runs because the background color of the image seems to average. I would like to set it all to black (the 0 values of the array). How would I go about doing this? I found this on Matplotlib's website , but it doesn't work when I try it (it keeps telling me it didn't expect a byte for colormap).

Also: Is there any way to further vectorize the while loop? I was thinking something along the lines of creating an array of 'mask' values that would indicate whether or not the values get evaluated. Trying to create another index yells at me for casting to multiple values.

Code:

# -*- coding: cp1252 -*-
from numpy import *
from math import *
import matplotlib.pyplot as plt

def fdtd():
    print 'Starting simulation.'
    # Define constants and parameters
    #mu0 = pi*4E-7 # pH/µm
    #e0 =   8.854187E-12 # Picofarads/micron
    e0 = 8.85418782E-6
    mu0 = 1.256637061
    c = 1/sqrt(mu0*e0)

    # Simulation Parameters
    cellsizeX = 100. #Size of Yee-cell x edge in microns
    cellsizeY = 100. #Size of Yee-cell y edge in microns
    numX = 200 #Number of cells in X direction
    numY = 200 #Number of cells in Y direction
    lengthX = cellsizeX * numX
    lengthY = cellsizeY * numY
    dx = cellsizeX
    dy = cellsizeY
    dt = 1/(c*sqrt(1/dx**2+1/dy**2))
    wavelength = 550E-9 #nm (green)
    freq = c/wavelength
    CEy = dt/(dx*mu0)
    CEx = dt/(dy*mu0)
    CHx = dt/(dy*e0)
    CHy = dt/(dx*e0)
    times = 1
    y = 0

    # Array creation
    print 'Creating arrays'
    E = zeros(shape=((2*numX+1),(2*numY+1)))
    Ep = E.copy()
    H = zeros(shape=(2*numX,2*numY))
    Hp = H.copy()
    Elec = E.copy()

    #Create indexes
    index = arange(0,2*numX, 1)
    xindex = arange(0, 2*numX-1, 2)
    yindex = arange(0, 2*numY-1, 2)
    print 'Entering simulation loop.'
    while times <= 500:
        y = 0
        # Initial Conditions
        if (times < 100):
            E[numX-50:numX+50,numY-50:numY+50] = times

        # Calculate H and E fields
        while y < len(yindex):
            Hp[xindex+1,yindex[y]+1] = H[xindex+1,yindex[y]+1] - CEy*(E[xindex+2,yindex[y]+1] - E[xindex,yindex[y]+1]) + CEx*(E[xindex+1,yindex[y]+2] - E[xindex+1,yindex[y]])
            Ep[xindex,yindex[y]+1] = E[xindex,yindex[y]+1] - CHy*(Hp[xindex+1,yindex[y]+1] - Hp[xindex-1, yindex[y]+1]) 
            Ep[xindex+1,yindex[y]] = E[xindex+1,yindex[y]] + CHx*(Hp[xindex+1, yindex[y]+1] - Hp[xindex+1,yindex[y]-1])
            y+=1

        # Boundary Conditions
        Ep[numX*2, :] = Ep[numX*2-1,:]
        Ep[:,numY*2] = Ep[:,numY*2-1]
        Ep[0,:] = Ep[1,:]
        Ep[:,0] = Ep[:,1]

        #Name switching
        E, Ep, H, Hp = Ep, E, Hp, H

        #Plotting and Saving
        plt.imshow(E[:,:], cmap = 'spectral')
        filename = str('PATH\%03d' % times) + '.png'
        plt.savefig(filename)
        plt.clf()
        times += 1

if __name__ == '__main__':
    fdtd()

Also: I've never had to put that coding line at the top until I switched to Eclipse as my IDE. Why is this now necessary?

Answer 1

Use this:

plt.imshow(E[:,:], cmap = 'spectral', vmin=0)

to prevent it from using the lowest value in your array as the lowest value in the colormap. There's also the vmax parameter if you want to keep the same colormap through each step.