from yeecuda import YeeCuda
# Macros
indexed = lambda l, offset=0: zip(np.arange(len(l)) + offset, l)
def gauss(fdtd):
width = (2 * np.power(fdtd.tal, 2))
func = lambda t: np.exp(-np.power(t - 2 * fdtd.t0, 2) / width)
for k, t in indexed(fdtd.td):
fdtd.Ez[k, :, 2] = func(t)
#sim = YeeCuda('./caixa.png', verbose=False)
sim = YeeCuda([200, 200], verbose=False)
cpu = []
omp = []
gpu = []
iters = 800
print "Starting benchmark with %d iterations." % iters
for s in range(5):
sim.setFreq(2.4E9)
sim.bound['Ez'][:, 2] = 0
sim.bound['Ez'][1, :] = 2
sim.bound['Ez'][:, 1] = 2
sim.bound['Ez'][-1, :] = 2
sim.bound['Ez'][:, -1] = 2
t = sim.run(gauss, t=iters, proc="CPU")
cpu.append(t)
sim.setFreq(2.4E9)
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from yeecuda import YeeCuda
# Macros
indexed = lambda l, offset=0: zip(np.arange(len(l))+offset,l)
def gauss(fdtd):
width = (2*np.power(fdtd.tal,2))
func = lambda t: np.exp(-np.power(t-2*fdtd.t0,2) / width)
for k,t in indexed(fdtd.td):
fdtd.Ez[k,:,2] = func(t)
#sim = YeeCuda('./caixa.png', verbose=False)
sim = YeeCuda([200,200], verbose=False)
cpu = []
omp = []
gpu = []
iters = 800
print "Starting benchmark with %d iterations." % iters
for s in range(5):
sim.setFreq(2.4E9)
sim.bound['Ez'][:,2] = 0
sim.bound['Ez'][1,:] = 2
sim.bound['Ez'][:,1] = 2
sim.bound['Ez'][-1,:] = 2
sim.bound['Ez'][:,-1] = 2
t = sim.run(gauss,t=iters,proc="CPU")
cpu.append(t)
sim.setFreq(2.4E9)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from yeecuda import YeeCuda
# Macros
indexed = lambda l, offset=0: zip(np.arange(len(l))+offset,l)
def gauss(fdtd):
width = (2*np.power(fdtd.tal,2))
func = lambda t: np.exp(-np.power(t-2*fdtd.t0,2) / width)
for k,t in indexed(fdtd.td):
fdtd.Ez[k,:,2] = func(t)
sim = YeeCuda('./caixa.png', verbose=False)
sim.setFreq(2.4E9)
sim.bound['Ez'][:,2] = 0
sim.bound['Ez'][1,:] = 2
sim.bound['Ez'][:,1] = 2
sim.bound['Ez'][-1,:] = 2
sim.bound['Ez'][:,-1] = 2
t = sim.run(gauss,t=800,proc="GPU")
#%%Plot
def anim1D(vector, time=None):
time = np.arange(len(vector[:,0])) if time is None else time
fig, ax = plt.subplots()
line, = ax.plot(vector[0,:])
plt.ylim(vector.min(),vector.max())
plt.grid()