def test_09(self):
fg, ax = plot.imview(self.z,
title='Imview Test',
fltscl=True,
cbar=None)
ax.format_coord(0, 0)
plot.close(fg)
def test_11(self):
fig = plot.figure()
plot.imview((100.0 * self.z).astype(np.uint16),
title='Imview Test',
fltscl=True,
fgrf=fig)
plot.close()
def test_06(self):
x = np.linspace(-1, 1, 20)[np.newaxis, :]
y = np.linspace(-1, 1, 20)[:, np.newaxis]
z = np.sqrt(x**2 + y**2)
fig = plot.figure()
plot.imview(z, title='Imview Test', fgrf=fig)
plot.close()
def test_03(self):
x = np.linspace(-1, 1, 20)[np.newaxis, :]
y = np.linspace(-1, 1, 20)[:, np.newaxis]
z = np.sqrt(x**2 + y**2)
fig = plot.figure()
plot.surf(z, title='Surf Test', xlbl='x', ylbl='y', zlbl='z')
plot.close()
def saveXimg(cri, Xr, filename):
# print(Xr.shape)
X = np.sum(abs(Xr), axis=cri.axisM).squeeze()
fig = plot.figure(figsize=(7, 7))
plot.imview(X, cmap=plot.cm.Blues, fig=fig)
fig.savefig(filename)
plot.close()
mplot.close()
def test_07(self):
x = np.linspace(-1, 1, 20)[np.newaxis, :]
y = np.linspace(-1, 1, 20)[:, np.newaxis]
z = np.sqrt(x**2 + y**2)
z3 = np.dstack((z, 2 * z, 3 * z))
fig = plot.figure()
plot.imview(z3, title='Imview Test', fgrf=fig)
plot.close()
def saveXhist(Xr, filename):
Xr_ = abs(Xr.flatten())
fig = plot.figure(figsize=(7 * 10, 7))
ax = fig.add_subplot(1, 1, 1)
ax.hist(Xr_, bins=500, density=True)
fig.savefig(filename)
plot.close()
mplot.close()
def test_06(self):
fig = plot.figure()
plot.contour(self.z,
title='Contour Test',
xlbl='x',
ylbl='y',
fgrf=fig)
plot.close()
def test_05(self):
plot.contour(self.z,
x=self.x,
y=self.y,
title='Contour Test',
xlbl='x',
ylbl='y')
plot.close()
def test_04(self):
fig = plot.figure()
plot.surf(self.z,
x=self.x,
y=self.y,
title='Surf Test',
xlbl='x',
ylbl='y',
zlbl='z',
fgrf=fig)
plot.close()
def test_03(self):
fig, ax = plot.subplots(nrows=1, ncols=1)
plot.surf(self.z,
title='Surf Test',
xlbl='x',
ylbl='y',
zlbl='z',
elev=0.0,
fgrf=fig,
axrf=ax)
plot.close()
def saveimg2D(imgs, filename, titles=None):
if imgs.ndim == 3:
imgs = np.array([imgs])
if titles is not None and titles.ndim == 3:
titles = np.array([titles])
R = imgs.shape[0]
C = imgs.shape[1]
fig = plot.figure(figsize=(7 * C, 7 * R))
for r in range(R):
for c in range(C):
ax = fig.add_subplot(R, C, r * C + c + 1)
s = None
if titles is not None:
s = titles[r][c]
plot.imview(imgs[r][c], title=s, fig=fig, ax=ax)
plot.savefig(filename)
plot.close()
mplot.close()
def test_02(self):
x = np.linspace(-1, 1, 20)
y = x**2
fig = plot.figure()
plot.plot(y, x=x, title='Plot Test', xlbl='x', ylbl='y', fgrf=fig)
plot.close()
def test_01(self):
x = np.linspace(-1, 1, 20)
y = x**2
plot.plot(y, title='Plot Test', xlbl='x', ylbl='y', lgnd=('Legend'))
plot.close()
def test_12(self):
z3 = np.dstack((self.z, 2 * self.z, 3 * self.z))
fig = plot.figure()
plot.imview(z3, title='Imview Test', fgrf=fig)
plot.close()
def nakashizuka_solve(
cri, Dr0, Xr, Sr,
final_sigma,
maxitr = 40,
param_mu = 1,
param_lambda = 1e-2,
debug_dir = None
):
param_rho = 0.5
Xr = Xr.copy()
Sr = Sr.copy()
Dr = Dr0.copy()
Hr = np.zeros_like(cnvrep.zpad(Dr, cri.Nv))
Sf = to_frequency(cri, Sr)
# sigma set
# sigma_list = []
# sigma_list.append(Xr.max()*4)
# for i in range(7):
# sigma_list.append(sigma_list[i]*0.5)
first_sigma = Xr.max()*4
c = (final_sigma / first_sigma) ** (1/(maxitr - 1))
print("c = %.8f" % c)
sigma_list = []
sigma_list.append(first_sigma)
for i in range(maxitr - 1):
sigma_list.append(sigma_list[i]*c)
crop_op = []
for l in Dr.shape:
crop_op.append(slice(0, l))
crop_op = tuple(crop_op)
Pcn = cnvrep.getPcn(Dr.shape, cri.Nv, cri.dimN, cri.dimCd, zm=False)
updcnt = 0
dictcnt = 0
for sigma in sigma_list:
print("sigma = %.8f" % sigma)
# Xf_old = sl.rfftn(Xr, cri.Nv, cri.axisN)
for l in range(1):
# print("l0norm: %f" % l0norm(Xr, sigma_list[-1]))
# print('error1: ', l2norm(Sr - reconstruct(cri, Dr, Xr)))
# print("l2(Xr): %.6f, l2(delta): %.6f" % (l2norm(Xr), l2norm(delta)))
delta = Xr * np.exp(-(Xr*Xr) / (2*sigma*sigma))
Xr = Xr - param_mu*delta# + np.random.randn(*Xr.shape)*sigma*1e-1
Xf = to_frequency(cri, Xr)
# print('error2: ', l2norm(Sr - reconstruct(cri, Dr, Xr)))
Df = to_frequency(cri, Dr)
b = Xf / param_lambda + np.conj(Df) * Sf
Xf = sl.solvedbi_sm(Df, 1/param_lambda, b, axis=cri.axisM)
Xr = to_spatial(cri, Xf).astype(np.float32)
# print('error3: ', l2norm(Sr - reconstruct(cri, Dr, Xr)))
# save_reconstructed(cri, Dr, Xr, Sr, "./rec/%da.png" % reccnt)
# saveXhist(Xr, "./hist/%da.png" % reccnt)
Dr, Hr = update_dict(cri, Pcn, crop_op, Xr, Dr, Hr, Sf, param_rho)
Df = to_frequency(cri, Dr)
# print('error4: ', l2norm(Sr - reconstruct(cri, Dr, Xr)))
# # project X to solution space
# b = sl.inner(Df, Xf, axis=cri.axisM) - Sf
# c = sl.inner(Df, np.conj(Df), axis=cri.axisM)
# Xf = Xf - np.conj(Df) / c * b
# Xr = sl.irfftn(Xf, s=cri.Nv, axes=cri.axisN)
# print('error5: ', l2norm(Sr - reconstruct(cri, Dr, Xr)))
if debug_dir is not None:
saveimg(util.tiledict(Dr.squeeze()), debug_dir + "/dict/%d.png" % updcnt)
updcnt += 1
# saveXhist(Xr, "Xhist_sigma=" + str(sigma) + ".png")
# print("l0 norm of final X: %d" % smoothedl0norm(Xr, 0.00001))
plot.close()
mplot.close()
return Dr
def mysolve(
cri, Dr0, Xr, Sr,
final_sigma,
maxitr = 40,
param_mu = 1,
debug_dir = None
):
Dr = Dr0.copy()
Xr = Xr.copy()
Sr = Sr.copy()
Df = sl.rfftn(Dr, s=cri.Nv, axes=cri.axisN)
Sf = sl.rfftn(Sr, s=cri.Nv, axes=cri.axisN)
Xf = sl.rfftn(Xr, s=cri.Nv, axes=cri.axisN)
alpha = 1e0
# sigma set
first_sigma = Xr.max()*4
c = (final_sigma / first_sigma) ** (1/(maxitr - 1))
print("c = %.8f" % c)
sigma_list = []
sigma_list.append(first_sigma)
for i in range(maxitr - 1):
sigma_list.append(sigma_list[i]*c)
print(sigma_list[-1])
crop_op = []
for l in Dr.shape:
crop_op.append(slice(0, l))
crop_op = tuple(crop_op)
Pcn = cnvrep.getPcn(Dr.shape, cri.Nv, cri.dimN, cri.dimCd, zm=False)
updcnt = 0
for sigma in sigma_list:
print("sigma = %.8f" % sigma)
# Xf_old = sl.rfftn(Xr, cri.Nv, cri.axisN)
# print("l0norm: %f" % l0norm(Xr, sigma_list[-1]))
# print('error1: ', l2norm(Sr - reconstruct(cri, Dr, Xr)))
delta = Xr * np.exp(-(Xr*Xr) / (2*sigma*sigma))
# print("l2(Xr): %.6f, l2(delta): %.6f" % (l2norm(Xr), l2norm(delta)))
Xr = Xr - param_mu*delta# + np.random.randn(*Xr.shape)*sigma*1e-1
Xf = sl.rfftn(Xr, cri.Nv, cri.axisN)
# saveXhist(Xr, "./hist/%db.png" % reccnt)
# print('error2: ', l2norm(Sr - reconstruct(cri, Dr, Xr)))
# if debug_dir is not None:
# save_reconstructed(cri, Dr, Xr, Sr, debug_dir + '/%drecA.png' % updcnt)
# DXf = sl.inner(Df, Xf, axis=cri.axisM)
# gamma = (np.sum(np.conj(DXf) * Sf, axis=cri.axisN, keepdims=True) + np.sum(DXf * np.conj(Sf), axis=cri.axisN, keepdims=True)) / 2 / np.sum(np.conj(DXf) * DXf, axis=cri.axisN, keepdims=True)
# print(gamma)
# print(gamma.shape, ' * ', Xr.shape)
# gamma = np.real(gamma)
# Xr = Xr * gamma
# Xf = to_frequency(cri, Xr)
# if debug_dir is not None:
# save_reconstructed(cri, Dr, Xr, Sr, debug_dir + '/%drecB.png' % updcnt)
# print('error3: ', l2norm(Sr - reconstruct(cri, Dr, Xr)))
# print("max: ", np.max(Xr))
B = sl.inner(Xf, Df, axis=cri.axisM) - Sf
derivDf = sl.inner(np.conj(Xf), B, axis=cri.axisK)
# derivDr = sl.irfftn(derivDf, s=cri.Nv, axes=cri.axisN)[crop_op]
def func(alpha):
Df_ = Df - alpha * derivDf
Dr_ = sl.irfftn(Df_, s=cri.Nv, axes=cri.axisN)[crop_op]
Df_ = sl.rfftn(Dr_, s=cri.Nv, axes=cri.axisN)
Sf_ = sl.inner(Df_, Xf, axis=cri.axisM)
return l2norm(Sr - sl.irfftn(Sf_, s=cri.Nv, axes=cri.axisN))
choice = np.array([func(alpha / 2), func(alpha), func(alpha * 2)]).argmin()
alpha *= [0.5, 1, 2][choice]
print("alpha: ", alpha)
Df = Df - alpha * derivDf
Dr = Pcn(sl.irfftn(Df, s=cri.Nv, axes=cri.axisN))[crop_op]
# print(l2norm(Dr.T[0]))
# Dr = normalize(Dr, axis=cri.axisN)
print(l2norm(Dr.T[0]))
Df = sl.rfftn(Dr, s=cri.Nv, axes=cri.axisN)
if debug_dir is not None:
saveimg(util.tiledict(Dr.squeeze()), debug_dir + "/dict/%d.png" % updcnt)
# if debug_dir is not None:
# save_reconstructed(cri, Dr, Xr, Sr, debug_dir + '/%drecC.png' % updcnt)
# dictcnt += 1
# print('error4: ', l2norm(Sr - reconstruct(cri, Dr, Xr)))
# save_reconstructed(cri, Dr, Xr, Sr, debug_dir + "/rec/%dc.png" % updcnt)
# project X to solution space
b = sl.inner(Df, Xf, axis=cri.axisM) - Sf
c = sl.inner(Df, np.conj(Df), axis=cri.axisM)
Xf = Xf - np.conj(Df) / c * b
Xr = sl.irfftn(Xf, s=cri.Nv, axes=cri.axisN)
# save_reconstructed(cri, Dr, Xr, Sr, debug_dir + "rec/%dd.png" % updcnt)
# saveXhist(Xr, debug_dir + "hist/%db.png" % updcnt)
updcnt += 1
# print("l0 norm of final X: %d" % smoothedl0norm(Xr, 0.00001))
plot.close()
mplot.close()
return Dr
def test_08(self):
fig = plot.figure()
plot.imview(self.z, title='Imview Test', fltscl=True, fgrf=fig)
plot.close()
def test_07(self):
plot.imview(self.z.astype(np.float16), title='Imview Test', cbar=True)
plot.close()
def test_05(self):
x = np.linspace(-1, 1, 20)[np.newaxis, :]
y = np.linspace(-1, 1, 20)[:, np.newaxis]
z = np.sqrt(x**2 + y**2)
plot.imview(np.asarray(z, np.float16), title='Imview Test', cbar=True)
plot.close()
def test_03(self):
fig = plot.figure()
plot.surf(self.z, title='Surf Test', xlbl='x', ylbl='y', zlbl='z')
plot.close()
def saveimg(img, filename, title=None):
fig = plot.figure(figsize=(7, 7))
plot.imview(img, fig=fig)
fig.savefig(filename)
plot.close()
mplot.close()
