def test_13(self):
N = 16
Nd = 5
Cs = 3
M = 4
D = np.random.randn(Nd, Nd, M)
s = np.random.randn(N, N, Cs)
lmbda = 1e-1
try:
b = cbpdn.ConvBPDNJoint(D, s, lmbda, dimK=0)
b.solve()
except Exception as e:
print(e)
assert (0)
def test_14(self):
N = 16
Nd = 5
Cs = 3
M = 4
D = np.random.randn(Nd, Nd, M)
s = np.random.randn(N, N, Cs)
lmbda = 1e-1
try:
opt = cbpdn.ConvBPDNJoint.Options({'LinSolveCheck': True})
b = cbpdn.ConvBPDNJoint(D, s, lmbda, opt=opt, dimK=0)
b.solve()
except Exception as e:
print(e)
assert 0
assert np.array(b.getitstat().XSlvRelRes).max() < 1e-5
def test_15(self):
N = 16
Nd = 5
K = 2
M = 4
D = np.random.randn(Nd, Nd, M)
s = np.random.randn(N, N, K)
dt = np.float32
opt = cbpdn.ConvBPDNJoint.Options({'Verbose' : False,
'MaxMainIter' : 20, 'AutoRho' : {'Enabled' : True},
'DataType' : dt})
lmbda = 1e-1
mu = 1e-2
b = cbpdn.ConvBPDNJoint(D, s, lmbda, mu, opt=opt)
b.solve()
assert(b.X.dtype == dt)
assert(b.Y.dtype == dt)
assert(b.U.dtype == dt)
"""
Set :class:`.admm.cbpdn.ConvBPDNJoint` solver options.
"""
lmbda = 1e-1
mu = 1e-2
opt = cbpdn.ConvBPDNJoint.Options({'Verbose': True, 'MaxMainIter': 200,
'RelStopTol': 5e-3, 'AuxVarObj': False})
"""
Initialise and run CSC solver.
"""
b = cbpdn.ConvBPDNJoint(D, sh, lmbda, mu, opt, dimK=0)
X = b.solve()
print("ConvBPDNJoint solve time: %.2fs" % b.timer.elapsed('solve'))
"""
Reconstruct image from sparse representation.
"""
shr = b.reconstruct().squeeze()
imgr = sl + shr
print("Reconstruction PSNR: %.2fdB\n" % sm.psnr(img, imgr))
"""
Display low pass component and sum of absolute values of coefficient maps of highpass component.
zm=optd['ZeroMean'])
"""
Update D update options to include initial values for Y and U.
"""
optd.update({'Y0': cnvrep.zpad(cnvrep.stdformD(D0n, cri.Cd, cri.M), cri.Nv),
'U0': np.zeros(cri.shpD)})
"""
Create X update object.
"""
xstep = cbpdn.ConvBPDNJoint(D0n, sh, lmbda, mu, optx)
"""
Create D update object.
"""
dstep = ccmod.ConvCnstrMOD(None, sh, D0.shape, optd, method='ism')
"""
Create DictLearn object and solve.
"""
opt = dictlrn.DictLearn.Options({'Verbose': True, 'MaxMainIter': 100})
d = dictlrn.DictLearn(xstep, dstep, opt)
def gengraphs(pth, nopyfftw):
"""
Generate call graph images when necessary. Parameter pth is the path
to the directory in which images are to be created. Parameter nopyfftw
is a flag indicating whether it is necessary to avoid using pyfftw.
"""
srcmodflt = '^sporco.admm'
srcqnmflt = r'^((?!<locals>|__new|_name_nested).)*$'
dstqnmflt = r'^((?!<locals>|__new|_name_nested).)*$'
fnmsub = ('^sporco.admm.', '')
grpflt = r'^[^\.]*.[^\.]*'
lnksub = (r'^([^\.]*).(.*)',
r'../../sporco.admm.\1.html#sporco.admm.\1.\2')
fntsz = 9
fntfm = 'Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans'
kwargs = {'fntsz': fntsz, 'fntfm': fntfm, 'rmsz': True}
ct = jonga.CallTracer(srcmodflt=srcmodflt,
srcqnmflt=srcqnmflt,
dstqnmflt=dstqnmflt,
fnmsub=fnmsub,
grpflt=grpflt,
lnksub=lnksub)
# Make destination directory if it doesn't exist
if not os.path.exists(pth):
os.makedirs(pth, exist_ok=True)
# Handle environment in which pyfftw is unavailable
if nopyfftw:
import numpy.fft as npfft
import sporco.linalg as spl
def empty(shape, dtype, order='C', n=None):
return np.zeros(shape, dtype=dtype)
spl.pyfftw_empty_aligned = empty
def rfftn_empty(shape, axes, dtype, order='C', n=None):
ashp = list(shape)
raxis = axes[-1]
ashp[raxis] = ashp[raxis] // 2 + 1
cdtype = spl.complex_dtype(dtype)
return np.zeros(ashp, dtype=cdtype)
spl.pyfftw_rfftn_empty_aligned = rfftn_empty
spl.fftn = npfft.fftn
spl.ifftn = npfft.ifftn
spl.rfftn = npfft.rfftn
spl.irfftn = npfft.irfftn
import numpy as np
np.random.seed(12345)
#### bpdn module
from sporco.admm import bpdn
mdnm = 'sporco.admm.bpdn'
D = np.random.randn(8, 16)
s = np.random.randn(8, 1)
lmbda = 0.1
## BPDN class
opt = bpdn.BPDN.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'bpdn_init.svg', **kwargs):
b = bpdn.BPDN(D, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'bpdn_solve.svg', **kwargs):
b.solve()
## BPDNJoint class
opt = bpdn.BPDNJoint.Options({'Verbose': False, 'MaxMainIter': 1})
mu = 0.01
with CallGraph(ct, mdnm, pth, 'bpdnjnt_init.svg', **kwargs):
b = bpdn.BPDNJoint(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'bpdnjnt_solve.svg', **kwargs):
b.solve()
## ElasticNet class
opt = bpdn.ElasticNet.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'elnet_init.svg', **kwargs):
b = bpdn.ElasticNet(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'elnet_solve.svg', **kwargs):
b.solve()
# BPDNProjL1 class
opt = bpdn.BPDNProjL1.Options({'Verbose': False, 'MaxMainIter': 1})
gamma = 2.0
with CallGraph(ct, mdnm, pth, 'bpdnprjl1_init.svg', **kwargs):
b = bpdn.BPDNProjL1(D, s, gamma, opt)
with CallGraph(ct, mdnm, pth, 'bpdnprjl1_solve.svg', **kwargs):
b.solve()
## MinL1InL2Ball class
opt = bpdn.MinL1InL2Ball.Options({'Verbose': False, 'MaxMainIter': 1})
epsilon = 1.0
with CallGraph(ct, mdnm, pth, 'bpdnml1l2_init.svg', **kwargs):
b = bpdn.MinL1InL2Ball(D, s, epsilon, opt)
with CallGraph(ct, mdnm, pth, 'bpdnml1l2_solve.svg', **kwargs):
b.solve()
#### cbpdn module
from sporco.admm import cbpdn
mdnm = 'sporco.admm.cbpdn'
D = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8)
lmbda = 0.1
## ConvBPDN class
opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdn_init.svg', **kwargs):
b = cbpdn.ConvBPDN(D, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'cbpdn_solve.svg', **kwargs):
b.solve()
## ConvBPDNJoint class
opt = cbpdn.ConvBPDNJoint.Options({'Verbose': False, 'MaxMainIter': 1})
mu = 0.01
with CallGraph(ct, mdnm, pth, 'cbpdnjnt_init.svg', **kwargs):
b = cbpdn.ConvBPDNJoint(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnjnt_solve.svg', **kwargs):
b.solve()
## ConvElasticNet class
opt = cbpdn.ConvElasticNet.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'celnet_init.svg', **kwargs):
b = cbpdn.ConvElasticNet(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'celnet_solve.svg', **kwargs):
b.solve()
## ConvBPDNGradReg class
opt = cbpdn.ConvBPDNGradReg.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdngrd_init.svg', **kwargs):
b = cbpdn.ConvBPDNGradReg(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdngrd_solve.svg', **kwargs):
b.solve()
## ConvBPDNProjL1 class
opt = cbpdn.ConvBPDNProjL1.Options({'Verbose': False, 'MaxMainIter': 1})
gamma = 0.5
with CallGraph(ct, mdnm, pth, 'cbpdnprjl1_init.svg', **kwargs):
b = cbpdn.ConvBPDNProjL1(D, s, gamma, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnprjl1_solve.svg', **kwargs):
b.solve()
## ConvMinL1InL2Ball class
opt = cbpdn.ConvMinL1InL2Ball.Options({'Verbose': False, 'MaxMainIter': 1})
epsilon = 0.5
with CallGraph(ct, mdnm, pth, 'cbpdnml1l2_init.svg', **kwargs):
b = cbpdn.ConvMinL1InL2Ball(D, s, epsilon, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnml1l2_solve.svg', **kwargs):
b.solve()
## ConvBPDNMaskDcpl class
opt = cbpdn.ConvBPDNMaskDcpl.Options({'Verbose': False, 'MaxMainIter': 1})
W = np.ones(s.shape)
with CallGraph(ct, mdnm, pth, 'cbpdnmd_init.svg', **kwargs):
b = cbpdn.ConvBPDNMaskDcpl(D, s, lmbda, W, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnmd_solve.svg', **kwargs):
b.solve()
#### cbpdntv module
from sporco.admm import cbpdntv
mdnm = 'sporco.admm.cbpdntv'
D = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8)
lmbda = 0.1
mu = 0.01
## ConvBPDNScalarTV class
opt = cbpdntv.ConvBPDNScalarTV.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'cbpdnstv_init.svg', **kwargs):
b = cbpdntv.ConvBPDNScalarTV(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnstv_solve.svg', **kwargs):
b.solve()
## ConvBPDNVectorTV class
opt = cbpdntv.ConvBPDNVectorTV.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'cbpdnvtv_init.svg', **kwargs):
b = cbpdntv.ConvBPDNVectorTV(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnvtv_solve.svg', **kwargs):
b.solve()
## ConvBPDNRecTV class
opt = cbpdntv.ConvBPDNRecTV.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdnrtv_init.svg', **kwargs):
b = cbpdntv.ConvBPDNRecTV(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnrtv_solve.svg', **kwargs):
b.solve()
#### cmod module
from sporco.admm import cmod
mdnm = 'sporco.admm.cmod'
X = np.random.randn(8, 16)
S = np.random.randn(8, 16)
## CnstrMOD class
opt = cmod.CnstrMOD.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cmod_init.svg', **kwargs):
b = cmod.CnstrMOD(X, S, opt=opt)
with CallGraph(ct, mdnm, pth, 'cmod_solve.svg', **kwargs):
b.solve()
#### ccmod module
from sporco.admm import ccmod
mdnm = 'sporco.admm.ccmod'
X = np.random.randn(8, 8, 1, 2, 1)
S = np.random.randn(8, 8, 2)
dsz = (4, 4, 1)
## ConvCnstrMOD_IterSM class
opt = ccmod.ConvCnstrMOD_IterSM.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodism_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD_IterSM(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodism_solve.svg', **kwargs):
b.solve()
## ConvCnstrMOD_CG class
opt = ccmod.ConvCnstrMOD_CG.Options({
'Verbose': False,
'MaxMainIter': 1,
'CG': {
'MaxIter': 1
}
})
with CallGraph(ct, mdnm, pth, 'ccmodcg_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD_CG(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodcg_solve.svg', **kwargs):
b.solve()
## ConvCnstrMOD_Consensus class
opt = ccmod.ConvCnstrMOD_Consensus.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodcnsns_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD_Consensus(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodcnsns_solve.svg', **kwargs):
b.solve()
#### ccmodmd module
from sporco.admm import ccmodmd
mdnm = 'sporco.admm.ccmodmd'
X = np.random.randn(8, 8, 1, 2, 1)
S = np.random.randn(8, 8, 2)
W = np.array([1.0])
dsz = (4, 4, 1)
## ConvCnstrMODMaskDcpl_IterSM class
opt = ccmodmd.ConvCnstrMODMaskDcpl_IterSM.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodmdism_init.svg', **kwargs):
b = ccmodmd.ConvCnstrMODMaskDcpl_IterSM(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdism_solve.svg', **kwargs):
b.solve()
## ConvCnstrMODMaskDcpl_CG class
opt = ccmodmd.ConvCnstrMODMaskDcpl_CG.Options({
'Verbose': False,
'MaxMainIter': 1,
'CG': {
'MaxIter': 1
}
})
with CallGraph(ct, mdnm, pth, 'ccmodmdcg_init.svg', **kwargs):
b = ccmodmd.ConvCnstrMODMaskDcpl_CG(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdcg_solve.svg', **kwargs):
b.solve()
## ConvCnstrMODMaskDcpl_Consensus class
opt = ccmodmd.ConvCnstrMODMaskDcpl_Consensus.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodmdcnsns_init.svg', **kwargs):
b = ccmodmd.ConvCnstrMODMaskDcpl_Consensus(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdcnsns_solve.svg', **kwargs):
b.solve()
#### bpdndl module
from sporco.admm import bpdndl
mdnm = 'sporco.admm.bpdndl'
D0 = np.random.randn(8, 8)
S = np.random.randn(8, 16)
lmbda = 0.1
## BPDNDictLearn class
opt = bpdndl.BPDNDictLearn.Options({
'Verbose': False,
'MaxMainIter': 1,
'AccurateDFid': True
})
with CallGraph(ct, mdnm, pth, 'bpdndl_init.svg', **kwargs):
b = bpdndl.BPDNDictLearn(D0, S, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'bpdndl_solve.svg', **kwargs):
b.solve()
#### cbpdndl module
from sporco.admm import cbpdndl
mdnm = 'sporco.admm.cbpdndl'
D0 = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8, 10)
lmbda = 0.1
## ConvBPDNDictLearn class
opt = cbpdndl.ConvBPDNDictLearn.Options({
'Verbose': False,
'MaxMainIter': 1,
'AccurateDFid': True
})
with CallGraph(ct, mdnm, pth, 'cbpdndl_init.svg', **kwargs):
b = cbpdndl.ConvBPDNDictLearn(D0, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'cbpdndl_solve.svg', **kwargs):
b.solve()
## ConvBPDNMaskDcplDictLearn class
W = np.array([1.0])
opt = cbpdndl.ConvBPDNMaskDcplDictLearn.Options({
'Verbose': False,
'MaxMainIter': 1,
'AccurateDFid': True
})
with CallGraph(ct, mdnm, pth, 'cbpdnmddl_init.svg', **kwargs):
b = cbpdndl.ConvBPDNMaskDcplDictLearn(D0, s, lmbda, W, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnmddl_solve.svg', **kwargs):
b.solve()
#### tvl1 module
from sporco.admm import tvl1
mdnm = 'sporco.admm.tvl1'
s = np.random.randn(16, 16)
lmbda = 0.1
## TVL1Denoise class
opt = tvl1.TVL1Denoise.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'tvl1den_init.svg', **kwargs):
b = tvl1.TVL1Denoise(s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl1den_solve.svg', **kwargs):
b.solve()
## TVL1Deconv class
opt = tvl1.TVL1Deconv.Options({'Verbose': False, 'MaxMainIter': 1})
h = np.random.randn(3, 3)
with CallGraph(ct, mdnm, pth, 'tvl1dcn_init.svg', **kwargs):
b = tvl1.TVL1Deconv(h, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl1dcn_solve.svg', **kwargs):
b.solve()
#### tvl2 module
from sporco.admm import tvl2
mdnm = 'sporco.admm.tvl2'
s = np.random.randn(16, 16)
lmbda = 0.1
## TVL2Denoise class
opt = tvl2.TVL2Denoise.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'tvl2den_init.svg', **kwargs):
b = tvl2.TVL2Denoise(s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl2den_solve.svg', **kwargs):
b.solve()
## TVL2Deconv class
opt = tvl2.TVL2Deconv.Options({'Verbose': False, 'MaxMainIter': 1})
h = np.random.randn(3, 3)
with CallGraph(ct, mdnm, pth, 'tvl2dcn_init.svg', **kwargs):
b = tvl2.TVL2Deconv(h, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl2dcn_solve.svg', **kwargs):
b.solve()
srcmodflt = '^sporco.fista'
fnmsub = ('^sporco.fista.', '')
lnksub = (r'^([^\.]*).(.*)',
r'../../sporco.fista.\1.html#sporco.fista.\1.\2')
ct = jonga.CallTracer(srcmodflt=srcmodflt,
srcqnmflt=srcqnmflt,
dstqnmflt=dstqnmflt,
fnmsub=fnmsub,
grpflt=grpflt,
lnksub=lnksub)
#### fista.cbpdn module
from sporco.fista import cbpdn
mdnm = 'sporco.fista.cbpdn'
D = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8)
lmbda = 0.1
## ConvBPDN class
opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'fista_cbpdn_init.svg', **kwargs):
b = cbpdn.ConvBPDN(D, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'fista_cbpdn_solve.svg', **kwargs):
b.solve()
#### fista.ccmod module
from sporco.fista import ccmod
mdnm = 'sporco.fista.ccmod'
X = np.random.randn(8, 8, 1, 2, 1)
S = np.random.randn(8, 8, 2)
dsz = (4, 4, 1)
## ConvCnstrMOD class
opt = ccmod.ConvCnstrMOD.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'ccmodfista_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodfista_solve.svg', **kwargs):
b.solve()
## ConvCnstrMODMaskDcpl class
opt = ccmod.ConvCnstrMODMask.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'ccmodmdfista_init.svg', **kwargs):
b = ccmod.ConvCnstrMODMask(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdfista_solve.svg', **kwargs):
b.solve()
imgnph.shape[0:2], (0, 1))
W = 1.0/(np.maximum(np.abs(W), 1e-8))
lmbda = 1.5e-2
mu = 2.7e-1
opt = cbpdn.ConvBPDNJoint.Options({'Verbose': True, 'MaxMainIter': 250,
'HighMemSolve': True, 'RelStopTol': 3e-3, 'AuxVarObj': False,
'L1Weight': W, 'AutoRho': {'Enabled': False}, 'rho': 1e3*lmbda})
"""
Initialise the :class:`.admm.cbpdn.ConvBPDNJoint` object and call the ``solve`` method.
"""
b = cbpdn.ConvBPDNJoint(D, pad(imgnh), lmbda, mu, opt, dimK=0)
X = b.solve()
"""
The denoised estimate of the image is just the reconstruction from the coefficient maps.
"""
imgdp = b.reconstruct().squeeze()
imgd = np.clip(crop(imgdp) + imgnl, 0, 1)
"""
Display solve time and denoising performance.
"""
def gengraphs(pth):
"""
Generate call graph images when necessary. Parameter pth is the path
to the directory in which images are to be created.
"""
srcmodflt = '^sporco.admm'
srcqnmflt = r'^((?!<locals>|__new|_name_nested).)*$'
dstqnmflt = r'^((?!<locals>|__new|_name_nested).)*$'
fnmsub = ('^sporco.admm.', '')
grpflt = r'^[^\.]*.[^\.]*'
lnkpfx = '../../modules/'
lnksub = (r'^([^\.]*).([^\.]*)(?:(.__init__|.__call__)|(.[^\.]*))',
lnkpfx + r'sporco.admm.\1.html#sporco.admm.\1.\2\4')
fntsz = 9
fntfm = 'Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans'
kwargs = {'fntsz': fntsz, 'fntfm': fntfm, 'rmsz': True}
ct = jonga.CallTracer(srcmodflt=srcmodflt,
srcqnmflt=srcqnmflt,
dstqnmflt=dstqnmflt,
fnmsub=fnmsub,
grpflt=grpflt,
lnksub=lnksub)
# Make destination directory if it doesn't exist
if not os.path.exists(pth):
os.makedirs(pth, exist_ok=True)
import numpy as np
np.random.seed(12345)
#### bpdn module
from sporco.admm import bpdn
mdnm = 'sporco.admm.bpdn'
D = np.random.randn(8, 16)
s = np.random.randn(8, 1)
lmbda = 0.1
## BPDN class
opt = bpdn.BPDN.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'bpdn_init.svg', **kwargs):
b = bpdn.BPDN(D, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'bpdn_solve.svg', **kwargs):
b.solve()
## BPDNJoint class
opt = bpdn.BPDNJoint.Options({'Verbose': False, 'MaxMainIter': 1})
mu = 0.01
with CallGraph(ct, mdnm, pth, 'bpdnjnt_init.svg', **kwargs):
b = bpdn.BPDNJoint(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'bpdnjnt_solve.svg', **kwargs):
b.solve()
## ElasticNet class
opt = bpdn.ElasticNet.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'elnet_init.svg', **kwargs):
b = bpdn.ElasticNet(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'elnet_solve.svg', **kwargs):
b.solve()
# BPDNProjL1 class
opt = bpdn.BPDNProjL1.Options({'Verbose': False, 'MaxMainIter': 1})
gamma = 2.0
with CallGraph(ct, mdnm, pth, 'bpdnprjl1_init.svg', **kwargs):
b = bpdn.BPDNProjL1(D, s, gamma, opt)
with CallGraph(ct, mdnm, pth, 'bpdnprjl1_solve.svg', **kwargs):
b.solve()
## MinL1InL2Ball class
opt = bpdn.MinL1InL2Ball.Options({'Verbose': False, 'MaxMainIter': 1})
epsilon = 1.0
with CallGraph(ct, mdnm, pth, 'bpdnml1l2_init.svg', **kwargs):
b = bpdn.MinL1InL2Ball(D, s, epsilon, opt)
with CallGraph(ct, mdnm, pth, 'bpdnml1l2_solve.svg', **kwargs):
b.solve()
#### cbpdn module
from sporco.admm import cbpdn
mdnm = 'sporco.admm.cbpdn'
D = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8)
lmbda = 0.1
## ConvBPDN class
opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdn_init.svg', **kwargs):
b = cbpdn.ConvBPDN(D, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'cbpdn_solve.svg', **kwargs):
b.solve()
## ConvBPDNJoint class
opt = cbpdn.ConvBPDNJoint.Options({'Verbose': False, 'MaxMainIter': 1})
mu = 0.01
with CallGraph(ct, mdnm, pth, 'cbpdnjnt_init.svg', **kwargs):
b = cbpdn.ConvBPDNJoint(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnjnt_solve.svg', **kwargs):
b.solve()
## ConvElasticNet class
opt = cbpdn.ConvElasticNet.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'celnet_init.svg', **kwargs):
b = cbpdn.ConvElasticNet(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'celnet_solve.svg', **kwargs):
b.solve()
## ConvBPDNGradReg class
opt = cbpdn.ConvBPDNGradReg.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdngrd_init.svg', **kwargs):
b = cbpdn.ConvBPDNGradReg(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdngrd_solve.svg', **kwargs):
b.solve()
## ConvBPDNProjL1 class
opt = cbpdn.ConvBPDNProjL1.Options({'Verbose': False, 'MaxMainIter': 1})
gamma = 0.5
with CallGraph(ct, mdnm, pth, 'cbpdnprjl1_init.svg', **kwargs):
b = cbpdn.ConvBPDNProjL1(D, s, gamma, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnprjl1_solve.svg', **kwargs):
b.solve()
## ConvMinL1InL2Ball class
opt = cbpdn.ConvMinL1InL2Ball.Options({'Verbose': False, 'MaxMainIter': 1})
epsilon = 0.5
with CallGraph(ct, mdnm, pth, 'cbpdnml1l2_init.svg', **kwargs):
b = cbpdn.ConvMinL1InL2Ball(D, s, epsilon, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnml1l2_solve.svg', **kwargs):
b.solve()
## ConvBPDNMaskDcpl class
opt = cbpdn.ConvBPDNMaskDcpl.Options({'Verbose': False, 'MaxMainIter': 1})
W = np.ones(s.shape)
with CallGraph(ct, mdnm, pth, 'cbpdnmd_init.svg', **kwargs):
b = cbpdn.ConvBPDNMaskDcpl(D, s, lmbda, W, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnmd_solve.svg', **kwargs):
b.solve()
## ConvL1L1Grd class
opt = cbpdn.ConvL1L1Grd.Options({'Verbose': False, 'MaxMainIter': 1})
mu = 1e-2
with CallGraph(ct, mdnm, pth, 'cl1l1grd_init.svg', **kwargs):
b = cbpdn.ConvL1L1Grd(D, s, lmbda, mu, W, opt)
with CallGraph(ct, mdnm, pth, 'cl1l1grd_solve.svg', **kwargs):
b.solve()
#### cbpdntv module
from sporco.admm import cbpdntv
mdnm = 'sporco.admm.cbpdntv'
D = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8)
lmbda = 0.1
mu = 0.01
## ConvBPDNScalarTV class
opt = cbpdntv.ConvBPDNScalarTV.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'cbpdnstv_init.svg', **kwargs):
b = cbpdntv.ConvBPDNScalarTV(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnstv_solve.svg', **kwargs):
b.solve()
## ConvBPDNVectorTV class
opt = cbpdntv.ConvBPDNVectorTV.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'cbpdnvtv_init.svg', **kwargs):
b = cbpdntv.ConvBPDNVectorTV(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnvtv_solve.svg', **kwargs):
b.solve()
## ConvBPDNRecTV class
opt = cbpdntv.ConvBPDNRecTV.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdnrtv_init.svg', **kwargs):
b = cbpdntv.ConvBPDNRecTV(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnrtv_solve.svg', **kwargs):
b.solve()
#### cbpdnin module
from sporco.admm import cbpdnin
mdnm = 'sporco.admm.cbpdnin'
D = np.random.randn(4, 4, 32)
s = np.random.randn(8, 8)
lmbda = 0.1
mu = 0.01
Wg = np.append(np.eye(16), np.eye(16), axis=-1)
## ConvBPDNInhib class
opt = cbpdnin.ConvBPDNInhib.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdnin_init.svg', **kwargs):
b = cbpdnin.ConvBPDNInhib(D,
s,
Wg,
Whn=4,
lmbda=lmbda,
mu=mu,
gamma=None,
opt=opt)
with CallGraph(ct, mdnm, pth, 'cbpdnin_solve.svg', **kwargs):
b.solve()
#### cmod module
from sporco.admm import cmod
mdnm = 'sporco.admm.cmod'
X = np.random.randn(8, 16)
S = np.random.randn(8, 16)
## CnstrMOD class
opt = cmod.CnstrMOD.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cmod_init.svg', **kwargs):
b = cmod.CnstrMOD(X, S, opt=opt)
with CallGraph(ct, mdnm, pth, 'cmod_solve.svg', **kwargs):
b.solve()
#### ccmod module
from sporco.admm import ccmod
mdnm = 'sporco.admm.ccmod'
X = np.random.randn(8, 8, 1, 2, 1)
S = np.random.randn(8, 8, 2)
dsz = (4, 4, 1)
## ConvCnstrMOD_IterSM class
opt = ccmod.ConvCnstrMOD_IterSM.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodism_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD_IterSM(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodism_solve.svg', **kwargs):
b.solve()
## ConvCnstrMOD_CG class
opt = ccmod.ConvCnstrMOD_CG.Options({
'Verbose': False,
'MaxMainIter': 1,
'CG': {
'MaxIter': 1
}
})
with CallGraph(ct, mdnm, pth, 'ccmodcg_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD_CG(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodcg_solve.svg', **kwargs):
b.solve()
## ConvCnstrMOD_Consensus class
opt = ccmod.ConvCnstrMOD_Consensus.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodcnsns_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD_Consensus(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodcnsns_solve.svg', **kwargs):
b.solve()
#### ccmodmd module
from sporco.admm import ccmodmd
mdnm = 'sporco.admm.ccmodmd'
X = np.random.randn(8, 8, 1, 2, 1)
S = np.random.randn(8, 8, 2)
W = np.array([1.0])
dsz = (4, 4, 1)
## ConvCnstrMODMaskDcpl_IterSM class
opt = ccmodmd.ConvCnstrMODMaskDcpl_IterSM.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodmdism_init.svg', **kwargs):
b = ccmodmd.ConvCnstrMODMaskDcpl_IterSM(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdism_solve.svg', **kwargs):
b.solve()
## ConvCnstrMODMaskDcpl_CG class
opt = ccmodmd.ConvCnstrMODMaskDcpl_CG.Options({
'Verbose': False,
'MaxMainIter': 1,
'CG': {
'MaxIter': 1
}
})
with CallGraph(ct, mdnm, pth, 'ccmodmdcg_init.svg', **kwargs):
b = ccmodmd.ConvCnstrMODMaskDcpl_CG(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdcg_solve.svg', **kwargs):
b.solve()
## ConvCnstrMODMaskDcpl_Consensus class
opt = ccmodmd.ConvCnstrMODMaskDcpl_Consensus.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodmdcnsns_init.svg', **kwargs):
b = ccmodmd.ConvCnstrMODMaskDcpl_Consensus(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdcnsns_solve.svg', **kwargs):
b.solve()
#### tvl1 module
from sporco.admm import tvl1
mdnm = 'sporco.admm.tvl1'
s = np.random.randn(16, 16)
lmbda = 0.1
## TVL1Denoise class
opt = tvl1.TVL1Denoise.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'tvl1den_init.svg', **kwargs):
b = tvl1.TVL1Denoise(s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl1den_solve.svg', **kwargs):
b.solve()
## TVL1Deconv class
opt = tvl1.TVL1Deconv.Options({'Verbose': False, 'MaxMainIter': 1})
h = np.random.randn(3, 3)
with CallGraph(ct, mdnm, pth, 'tvl1dcn_init.svg', **kwargs):
b = tvl1.TVL1Deconv(h, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl1dcn_solve.svg', **kwargs):
b.solve()
#### tvl2 module
from sporco.admm import tvl2
mdnm = 'sporco.admm.tvl2'
s = np.random.randn(16, 16)
lmbda = 0.1
## TVL2Denoise class
opt = tvl2.TVL2Denoise.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'tvl2den_init.svg', **kwargs):
b = tvl2.TVL2Denoise(s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl2den_solve.svg', **kwargs):
b.solve()
## TVL2Deconv class
opt = tvl2.TVL2Deconv.Options({'Verbose': False, 'MaxMainIter': 1})
h = np.random.randn(3, 3)
with CallGraph(ct, mdnm, pth, 'tvl2dcn_init.svg', **kwargs):
b = tvl2.TVL2Deconv(h, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl2dcn_solve.svg', **kwargs):
b.solve()
srcmodflt = '^sporco.fista'
fnmsub = ('^sporco.fista.', '')
lnksub = (r'^([^\.]*).([^\.]*)(?:(.__init__|.__call__)|(.[^\.]*))',
lnkpfx + r'sporco.fista.\1.html#sporco.fista.\1.\2\4')
ct = jonga.CallTracer(srcmodflt=srcmodflt,
srcqnmflt=srcqnmflt,
dstqnmflt=dstqnmflt,
fnmsub=fnmsub,
grpflt=grpflt,
lnksub=lnksub)
#### fista.cbpdn module
from sporco.fista import cbpdn
mdnm = 'sporco.fista.cbpdn'
D = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8)
lmbda = 0.1
## ConvBPDN class
opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'fista_cbpdn_init.svg', **kwargs):
b = cbpdn.ConvBPDN(D, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'fista_cbpdn_solve.svg', **kwargs):
b.solve()
#### fista.ccmod module
from sporco.fista import ccmod
mdnm = 'sporco.fista.ccmod'
X = np.random.randn(8, 8, 1, 2, 1)
S = np.random.randn(8, 8, 2)
dsz = (4, 4, 1)
## ConvCnstrMOD class
opt = ccmod.ConvCnstrMOD.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'ccmodfista_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodfista_solve.svg', **kwargs):
b.solve()
## ConvCnstrMODMask class
opt = ccmod.ConvCnstrMODMask.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'ccmodmdfista_init.svg', **kwargs):
b = ccmod.ConvCnstrMODMask(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdfista_solve.svg', **kwargs):
b.solve()
srcmodflt = '^sporco.dictlrn'
fnmsub = ('^sporco.dictlrn.', '')
lnksub = (r'^([^\.]*).([^\.]*)(?:(.__init__|.__call__)|(.[^\.]*))',
lnkpfx + r'sporco.dictlrn.\1.html#sporco.dictlrn.\1.\2\4')
ct = jonga.CallTracer(srcmodflt=srcmodflt,
srcqnmflt=srcqnmflt,
dstqnmflt=dstqnmflt,
fnmsub=fnmsub,
grpflt=grpflt,
lnksub=lnksub)
#### bpdndl module
from sporco.dictlrn import bpdndl
mdnm = 'sporco.dictlrn.bpdndl'
D0 = np.random.randn(8, 8)
S = np.random.randn(8, 16)
lmbda = 0.1
## BPDNDictLearn class
opt = bpdndl.BPDNDictLearn.Options({
'Verbose': False,
'MaxMainIter': 1,
'AccurateDFid': True
})
with CallGraph(ct, mdnm, pth, 'bpdndl_init.svg', **kwargs):
b = bpdndl.BPDNDictLearn(D0, S, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'bpdndl_solve.svg', **kwargs):
b.solve()
#### cbpdndl module
from sporco.dictlrn import cbpdndl
mdnm = 'sporco.dictlrn.cbpdndl'
D0 = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8, 10)
lmbda = 0.1
## ConvBPDNDictLearn class
opt = cbpdndl.ConvBPDNDictLearn.Options({
'Verbose': False,
'MaxMainIter': 1,
'AccurateDFid': True
})
with CallGraph(ct, mdnm, pth, 'cbpdndl_init.svg', **kwargs):
b = cbpdndl.ConvBPDNDictLearn(D0, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'cbpdndl_solve.svg', **kwargs):
b.solve()
#### cbpdndlmd module
from sporco.dictlrn import cbpdndlmd
mdnm = 'sporco.dictlrn.cbpdndlmd'
## ConvBPDNMaskDcplDictLearn class
W = np.array([1.0])
opt = cbpdndlmd.ConvBPDNMaskDictLearn.Options({
'Verbose': False,
'MaxMainIter': 1,
'AccurateDFid': True
})
with CallGraph(ct, mdnm, pth, 'cbpdnmddl_init.svg', **kwargs):
b = cbpdndlmd.ConvBPDNMaskDictLearn(D0, s, lmbda, W, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnmddl_solve.svg', **kwargs):
b.solve()
