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Commit aeb25645 authored by daniel.schellhorn's avatar daniel.schellhorn
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added non-relative path references to GetDate while implementing zip... 

added non-relative path references to GetDate while implementing zip extraction, adding SetProxyPythonPath.py in phase and links in experiments in phase
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proxtoolbox/experiments/phase/CDI_Experiment.py
View file @ aeb25645
import SetProxPythonPath
from proxtoolbox.experiments.phase.phaseExperiment import PhaseExperiment
from proxtoolbox.utils.mypoissonrnd import mypoissonrnd
from proxtoolbox.utils.gaussian import gaussian
......@@ -117,7 +119,7 @@ class CDI_Experiment(PhaseExperiment):
self.rt_data = Cell(1)
self.rt_data[0] = M
# standard for the main program is that
# standard for the main program is that
# the data field is the magnitude SQUARED
# in Luke.m this is changed to the magnitude.
self.data = Cell(1)
......@@ -127,10 +129,10 @@ class CDI_Experiment(PhaseExperiment):
self.support_idx = np.nonzero(S)
self.sets = 2
# use the abs_illumination field to represent the
# use the abs_illumination field to represent the
# support constraint.
self.abs_illumination = S
# initial guess
tmp_rnd = (np.random.rand(N, N)).T # to match Matlab
self.u0 = S * tmp_rnd
......
proxtoolbox/experiments/phase/CDP_Experiment.py
View file @ aeb25645
import SetProxPythonPath
from proxtoolbox.experiments.phase.phaseExperiment import PhaseExperiment
from proxtoolbox.utils import fft, ifft
from proxtoolbox.utils.loadMatFile import loadMatFile
......@@ -43,7 +44,7 @@ class CDP_Experiment(PhaseExperiment):
}
return defaultParams
def __init__(self,
def __init__(self,
warmup_iter = 0,
**kwargs):
"""
......@@ -66,14 +67,14 @@ class CDP_Experiment(PhaseExperiment):
"""
Load CDP dataset. Create the initial iterate.
"""
# Implementation of the Wirtinger Flow (WF) algorithm presented in the paper
# Implementation of the Wirtinger Flow (WF) algorithm presented in the paper
# "Phase Retrieval via Wirtinger Flow: Theory and algorithms"
# by E. J. Candes, X. Li, and M. Soltanolkotabi
# integrated into the ProxToolbox by
# integrated into the ProxToolbox by
# Russell Luke, September 2016.
# The input data are coded diffraction patterns about a random complex
# valued image.
# valued image.
# for debug:
# used predefined randomly generated data for the case where n1 = 128, n2 = 1
......@@ -84,7 +85,7 @@ class CDP_Experiment(PhaseExperiment):
debug_image = None
debug_masks = None
debug_z0 = None
# make image
if debug:
if n2 == 1:
......@@ -103,9 +104,9 @@ class CDP_Experiment(PhaseExperiment):
self.truth = x
self.norm_truth = norm(x,'fro')
self.truth_dim = x.shape
## Make masks and linear sampling operators
L = self.product_space_dimension # Number of masks
L = self.product_space_dimension # Number of masks
if debug:
masks = debug_masks
else:
......@@ -115,14 +116,14 @@ class CDP_Experiment(PhaseExperiment):
masks = np.random.choice(np.array([1j, -1j, 1, -1]),(L,n2))
else:
masks = np.random.choice(np.array([1j, -1j, 1, -1]),(n1,n2,L))
# Sample magnitudes and make masks
# Sample magnitudes and make masks
temp = random_sample(masks.shape) #works like rand but accepts tuple as argument
masks = masks * ( (temp <= 0.2)*sqrt(3) + (temp > 0.2)/sqrt(2) )
self.masks = conj(masks) # Saving the conjugate of the mask saves
# on computing the conjugate every time the
# mapping A (below) is applied.
# mapping A (below) is applied.
if n2 == 1:
# Make linear operators; A is forward map and At its scaled adjoint (At(Y)*numel(Y) is the adjoint)
A = lambda I: fft(conj(masks) * tile(I,[1, L])) # Input is n x 1 signal, output is n x L array
......@@ -134,16 +135,16 @@ class CDP_Experiment(PhaseExperiment):
else:
A = lambda I: fft2(masks * reshape(tile(I,[1, L]), (I.shape[0],I.shape[1], L), order='F')) # Input is n1 x n2 image, output is n1 x n2 x L array
At = lambda Y: mean(masks * ifft2(Y), 2).reshape((n1,n2)) # Input is n1 x n2 X L array, output is n1 x n2 image
# Support constraint: none
self.indicator_ampl = 1
self.abs_illumination = 1
self.illumination = 1
self.support_idx = np.nonzero(x.flatten(order = 'F')) # column-major order'
#alt_support_idx = find(Xi_A.T, 0, "!=")
# Data
# Data
if n2 == 1 or n1 == 1:
Y = abs(A(x))
else:
......@@ -157,28 +158,28 @@ class CDP_Experiment(PhaseExperiment):
self.rt_data = Y
Y = Y**2
self.data = Y
self.norm_data = sum(Y.flatten())/Y.size
normest = sqrt(self.norm_data) # Estimate norm to scale eigenvector
self.norm_data = sum(Y.flatten())/Y.size
normest = sqrt(self.norm_data) # Estimate norm to scale eigenvector
self.norm_rt_data = normest
# Initialization
npower_iter = self.warmup_iter # Number of power iterations
if debug:
# Initialization
npower_iter = self.warmup_iter # Number of power iterations
if debug:
z0 = debug_z0
else:
z0 = randn(n1,n2)
z0 = z0/norm(z0,'fro') # Initial guess
_tic = time.time()
# Power iterations
for _tt in range(npower_iter):
z0 = z0/norm(z0,'fro') # Initial guess
_tic = time.time()
# Power iterations
for _tt in range(npower_iter):
z0 = At(Y*A(z0))
z0 = z0/norm(z0)
_toc = time.time()
z = normest * z0 # Apply scaling
z = normest * z0 # Apply scaling
if n2 == 1:
_Relerrs = norm(x - exp(-1j*angle(trace(matmul(x.T.conj(), z)))) * z, 'fro')/norm(x,'fro')
self.u0 = tile(z,[1,L])
......@@ -188,7 +189,7 @@ class CDP_Experiment(PhaseExperiment):
else:
_Relerrs = norm(x - exp(-1j*angle(trace(matmul(x.T.conj(), z)))) * z, 'fro')/norm(x,'fro')
self.u0 = reshape(tile(z,[1, L]), (z.shape[0], z.shape[1], L), order='F') # order='F': to match Matlab
# The following is put here for now
if self.formulation == 'cyclic':
if self.Nx == 1:
......@@ -202,7 +203,7 @@ class CDP_Experiment(PhaseExperiment):
# converted from Matlab makes sense
self.product_space_dimension = self.sets
self.sets = 2
#print('Run time of initialization:', toc-tic, 's')
#print('Relative error after initialization:', Relerrs)
#print('\n')
......@@ -213,9 +214,9 @@ class CDP_Experiment(PhaseExperiment):
Determine the prox operators to be used for this experiment
"""
super(CDP_Experiment, self).setupProxOperators() # call parent's method
if self.formulation == 'cyclic':
self.proxOperators = []
self.proxOperators = []
self.nProx = self.sets
for _j in range(self.nProx):
self.proxOperators.append('P_CDP_cyclic')
......@@ -225,11 +226,11 @@ class CDP_Experiment(PhaseExperiment):
self.proxOperators = []
self.proxOperators.append('P_diag')
self.proxOperators.append('P_CDP')
self.propagator = 'Propagator_FreFra'
self.inverse_propagator = 'InvPropagator_FreFra'
def createTestImage(self):
"""
......@@ -247,8 +248,8 @@ class CDP_Experiment(PhaseExperiment):
2-D array masks
z0 : ndarray
initial guess
"""
"""
image = np.array([[-0.947246643957371 - 2.01621153886074j],\
[0.540149747070348 - 1.52496021647706j],\
[-0.216602140976276 + 0.458677002745254j],\
......@@ -506,7 +507,7 @@ class CDP_Experiment(PhaseExperiment):
[0.00000000000000 - 0.707106781186548j, -0.707106781186548 + 0.00000000000000j, 0.00000000000000 + 0.707106781186548j, 0.00000000000000 - 0.707106781186548j, 0.00000000000000 - 1.73205080756888j, 0.707106781186548 + 0.00000000000000j, 0.00000000000000 - 0.707106781186548j, 0.707106781186548 + 0.00000000000000j, -0.707106781186548 + 0.00000000000000j, 0.707106781186548 + 0.00000000000000j],\
[-0.707106781186548 + 0.00000000000000j, 0.00000000000000 + 0.707106781186548j, 0.00000000000000 - 0.707106781186548j, 0.707106781186548 + 0.00000000000000j, 0.00000000000000 + 0.707106781186548j, 0.00000000000000 + 0.707106781186548j, 0.00000000000000 + 0.707106781186548j, 0.00000000000000 + 1.73205080756888j, -0.707106781186548 + 0.00000000000000j, 0.00000000000000 + 1.73205080756888j],\
[-0.707106781186548 + 0.00000000000000j, 1.73205080756888 + 0.00000000000000j, 0.00000000000000 - 0.707106781186548j, 1.73205080756888 + 0.00000000000000j, -0.707106781186548 + 0.00000000000000j, 0.00000000000000 - 1.73205080756888j, -0.707106781186548 + 0.00000000000000j, 0.00000000000000 + 1.73205080756888j, 0.00000000000000 + 0.707106781186548j, 0.00000000000000 - 0.707106781186548j]])
z0 = np.array([0.0848802595989635,0.134817251982901,
-0.0550294990313983,0.0207696058654980,
0.0572518900492754,0.0304482413548984,
......@@ -573,6 +574,3 @@ class CDP_Experiment(PhaseExperiment):
0.0645047500348074,-0.0627622327830579])
z0 = np.reshape(z0, (128, 1)) # transform 1-D array z0 into 128x1 matrix
return image, masks, z0
proxtoolbox/experiments/phase/Elser_Experiment.py
View file @ aeb25645
import SetProxPythonPath
from proxtoolbox.experiments.phase.phaseExperiment import PhaseExperiment
from proxtoolbox import proxoperators
import numpy as np
......@@ -48,7 +49,7 @@ class Elser_Experiment(PhaseExperiment):
'graphics': 1
}
return defaultParams
def __init__(self, Atoms=100, category='E', **kwargs):
super(Elser_Experiment, self).__init__(**kwargs)
......@@ -67,21 +68,20 @@ class Elser_Experiment(PhaseExperiment):
"""
Load Elser dataset. Create the initial iterate.
"""
#make sure input data can be found, otherwise download it
#make sure input data can be found, otherwise download it
GetData.getData('Elser')
#defaultParams = getDefaultParameters()
# So far only Nx to set the desired
# resolution
newres = self.Nx
# The following value for snr does not work well in Python
# as this creates numerical issues with Poisson noise
# as this creates numerical issues with Poisson noise
# (data_ball is far too small)
# snr = self.data_ball
# Instead, we choose the following bigger value which
# Instead, we choose the following bigger value which
# works well.
snr = 1e-8
snr = 1e-8
print('Loading data.')
fn="".join(['../InputData/Phase/Elser/data', str(self.Atoms), self.category])
......@@ -99,7 +99,7 @@ class Elser_Experiment(PhaseExperiment):
self.rt_data = Cell(1)
self.rt_data[0] = M
# standard for the main program is that
# standard for the main program is that
# the data field is the magnitude SQUARED
# in Luke.m this is changed to the magnitude.
self.data = Cell(1)
......@@ -109,9 +109,9 @@ class Elser_Experiment(PhaseExperiment):
self.support_idx = np.nonzero(S)
self.sets = 2
# The support constraint is sparsity determined by the number of atoms indicated in the
# data file name...this needs to be installed
# The support constraint is sparsity determined by the number of atoms indicated in the
# data file name...this needs to be installed
# initial guess: follow Elser's initialization...
def setupProxOperators(self):
......@@ -135,12 +135,12 @@ class Elser_Experiment(PhaseExperiment):
self.nProx = self.sets
# Now we adjust data structures and prox operators according to the algorithm
# What follows wa set up for JWST and works in that context. Not
# What follows wa set up for JWST and works in that context. Not
# sure how much of this is trasferrable to Elsers problems (DRL 14.08.2020)
if self.algorithm_name == 'ADMM':
# set-up variables in cells for block-wise algorithms
# ADMM has three blocks of variables, primal, auxilliary (primal
# variables in the image space of some linear mapping) and dual.
# variables in the image space of some linear mapping) and dual.
# we sort these into a 3D cell.
u0 = self.u0
self.u0 = Cell(3)
......@@ -158,7 +158,7 @@ class Elser_Experiment(PhaseExperiment):
self.u0[1][j] = fft2(self.FT_conv_kernel[j]*tmp_u0) / (self.Nx*self.Ny)
self.u0[2][j] = self.u0[1][j] / self.lambda_0
self.proxOperators.append('Prox_primal_ADMM_indicator')
self.proxOperators.append('Approx_Pphase_FreFra_ADMM_Poisson')
self.proxOperators.append('Approx_Pphase_FreFra_ADMM_Poisson')
elif self.algorithm_name == 'AvP2':
# u_0 should be a cell...we change it into a cell of cells
u0 = self.u0
......@@ -170,7 +170,7 @@ class Elser_Experiment(PhaseExperiment):
P_Diag_prox = P_Diag_class(self)
tmp_u = P_Diag_prox.eval(self.u0[2])
self.u0[1] = tmp_u[0]
self.u0[0] = u0[self.product_space_dimension-1]
self.u0[0] = u0[self.product_space_dimension-1]
elif self.algorithm_name == 'PHeBIE':
# set up variables in cells for block-wise, implicit-explicit algorithms
u0 = self.u0
......@@ -195,7 +195,7 @@ class Elser_Experiment(PhaseExperiment):
self.masks = Cell(L)
for j in range(L):
self.masks[j] = self.indicator_ampl * self.FT_conv_kernel[j]
self.data_zeros = None
self.data_zeros = None
self.FT_conv_kernel = self.masks
self.proxOperators[1] = 'Approx_Pphase_JWST_Wirt'
self.use_farfield_formula = True
......@@ -206,7 +206,7 @@ class Elser_Experiment(PhaseExperiment):
Generate graphical output from the solution
"""
# display plot of far field data
# display plot of far field data
# figure(123)
f, (ax1) = subplots(1, 1,
figsize=(self.figure_width, self.figure_height),
......@@ -263,7 +263,7 @@ class Elser_Experiment(PhaseExperiment):
dpi = self.figure_dpi)
self.createImageSubFigure(f, ax1, abs(u), titles[0])
self.createImageSubFigure(f, ax2, real(u), titles[1])
ax3.semilogy(changes)
ax3.set_xlabel(label)
ax3.set_ylabel('Log of change in iterates')
......@@ -277,12 +277,10 @@ class Elser_Experiment(PhaseExperiment):
f.suptitle(title)
plt.subplots_adjust(hspace = 0.3) # adjust vertical space (height) between subplots (default = 0.2)
plt.subplots_adjust(wspace = 0.3) # adjust horizontal space (width) between subplots (default = 0.2)
show()
if __name__ == "__main__":
Elser = Elser_Experiment(Atoms=200, category='E', algorithm='RRR', lambda_0=0.95, lambda_max=0.95, anim=True)
Elser.run()
Elser.show()
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