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print_figs.py
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"""Print figures for
[1] M. J. Ehrhardt, P. J. Markiewicz, and C.-B. Schoenlieb,
Faster PET reconstruction with non-smooth priors by randomization and
preconditioning, Phys. Med. Biol., 2019. 10.1088/1361-6560/ab3d07"""
import numpy as np
import matplotlib.pyplot as plt
plt.switch_backend('Qt5Agg')
folder_odl = '/home/me404/store/repositories/github_myODL'
folder_out = '/home/me404/store/repositories/gitbb_SPDHG_Pawel/latex/pics'
folder_in = '/home/me404/store/projects/201804_SPDHG_PET/results'
import sys
sys.path.append(folder_odl)
import misc
#%%
formats = ['png']
filename = 'ml'
all_algs = ['MLEM', 'OSEM-21', 'OSEM-100', 'COSEM-252', 'SPDHG2-21', 'SPDHG2-100', 'SPDHG2-252']
nepoch = 30
dataset = 'fdg'
dataset = 'amyloid10min'
folder_main = '{}/{}_{}'.format(folder_in, filename, dataset)
folder_today = '{}/nepochs{}'.format(folder_main, nepoch)
file_target = '{}/target.npy'.format(folder_main)
x_opt, obj_opt, x_opt_smoothed = np.load(file_target, allow_pickle=True)
iter_save, niter, niter_per_epoch, image, out, nsub, prob = (
{}, {}, {}, {}, {}, {}, {})
for a in all_algs:
(iter_save[a], niter[a], niter_per_epoch[a], image[a], out[a],
nsub[a], prob[a]) = np.load('{}/npy/{}.npy'.format(folder_today, a),
allow_pickle=True)
epoch_save = {a: np.array(iter_save[a]) /
np.float(niter_per_epoch[a]) for a in all_algs}
out = misc.resort_out(out, obj_opt)
# draw figures
fig = []
algs = ['MLEM', 'OSEM-21', 'COSEM-252', 'SPDHG2-252']
labels = ['MLEM', 'OSEM', 'COSEM', r'SPDHG+$^\ast$']
lstyles = 3 * ['--'] + 1 * ['-']
misc.plots_paper_psnr(fig, [15, 55], [20, 30, 40, 50], epoch_save, out,
lstyles, algs, misc.colors_set(), labels, 1.1, 2)
algs = ['OSEM-21', 'OSEM-100', 'SPDHG2-21', 'SPDHG2-100']
labels = ['OSEM (21 subsets)', 'OSEM (100)',
r'SPDHG+$^\ast$ (21)', r'SPDHG+$^\ast$ (100)']
lstyles = 2 * ['--'] + 2 * ['-']
misc.plots_paper_psnr(fig, [15, 55], [20, 30, 40, 50], epoch_save, out,
lstyles, algs, misc.colors_pair(), labels, 1.1, 1)
#
for fmt in formats:
for i, fi in enumerate(fig):
fi.savefig('{}/{}_{}_{}_out{}.{}'.format(folder_out, fmt,
filename, dataset, i, fmt), bbox_inches='tight')
plt.close(fi)
#%%
filename = 'map_tv'
all_algs = ['PDHG1', 'PDHG2',
'SPDHG1-21-uni', 'SPDHG1-21-bal', 'SPDHG2-21-uni',
'SPDHG1-100-uni', 'SPDHG2-100-uni', 'SPDHG1-100-bal',
'SPDHG1-252-uni', 'SPDHG1-252-bal']
nepoch = 30
dataset = 'fdg'
alpha = 1.2
folder_main = '{}/{}_{}'.format(folder_in, filename, dataset)
folder_alpha = '{}/alpha{:.2g}'.format(folder_main, alpha)
folder_today = '{}/nepochs{}'.format(folder_alpha, nepoch)
file_target = '{}/target.npy'.format(folder_alpha)
x_opt, obj_opt = np.load(file_target, allow_pickle=True)
iter_save, niter, niter_per_epoch, image, out, nsub, prob = (
{}, {}, {}, {}, {}, {}, {})
for a in all_algs:
(iter_save[a], niter[a], niter_per_epoch[a], image[a], out[a],
nsub[a], prob[a]) = np.load('{}/npy/{}.npy'.format(folder_today, a),
allow_pickle=True)
epoch_save = {a: np.array(iter_save[a]) /
np.float(niter_per_epoch[a]) for a in all_algs}
out = misc.resort_out(out, obj_opt)
fig = []
algs = ['PDHG1', 'SPDHG1-21-uni', 'SPDHG1-100-uni', 'SPDHG1-252-uni']
labels = ['PDHG', 'SPDHG (21 subsets)', 'SPDHG (100)',
'SPDHG (252)']
lstyles = ['--'] + 3 * ['-']
misc.plots_paper(fig, [19, 35], [20, 25, 30, 35], epoch_save, out,
lstyles, algs, misc.colors_sequ(), labels, 1.1, 4)
algs = ['SPDHG1-21-uni', 'SPDHG1-21-bal',
'SPDHG1-100-uni', 'SPDHG1-100-bal',
'SPDHG1-252-uni', 'SPDHG1-252-bal']
labels = ['21 subsets, uniform sampling', '21, balanced',
'100, uniform', '100, balanced',
'252, uniform', '252, balanced']
lstyles = ['-'] * 6
misc.plots_paper(fig, [19, 48], [20, 30, 40], epoch_save, out,
lstyles, algs, misc.colors_pair(), labels, 1.2, 3)
algs = ['PDHG1', 'PDHG2', 'SPDHG1-21-uni', 'SPDHG2-21-uni',
'SPDHG1-100-uni', 'SPDHG2-100-uni']
labels = ['PDHG', 'PDHG (precond)', 'SPDHG (21 subsets)', 'SPDHG (21, precond)',
'SPDHG (100)', 'SPDHG (100, precond)']
lstyles = ['--'] * 2 + ['-'] * 4
misc.plots_paper(fig, [19, 35], [20, 25, 30, 35], epoch_save, out,
lstyles, algs, misc.colors_pair(), labels, 1.2, 3)
#
for fmt in formats:
for i, fi in enumerate(fig):
fi.savefig('{}/{}_{}_{}_out{}.{}'.format(folder_out, fmt,
filename, dataset, i, fmt), bbox_inches='tight')
plt.close(fi)
#%%
filename = 'map_atv'
all_algs = ['PDHG1', 'SPDHG2-21-bal', 'SPDHG2-100-bal', 'SPDHG2-252-bal']
nepoch = 30
dataset = 'amyloid10min'
alpha = 3
folder_main = '{}/{}_{}'.format(folder_in, filename, dataset)
folder_alpha = '{}/alpha{:.2g}'.format(folder_main, alpha)
folder_today = '{}/nepochs{}'.format(folder_alpha, nepoch)
file_target = '{}/target.npy'.format(folder_alpha)
x_opt, obj_opt = np.load(file_target, allow_pickle=True)
iter_save, niter, niter_per_epoch, image, out, nsub, prob = (
{}, {}, {}, {}, {}, {}, {})
for a in all_algs:
(iter_save[a], niter[a], niter_per_epoch[a], image[a], out[a],
nsub[a], prob[a]) = np.load('{}/npy/{}.npy'.format(folder_today, a),
allow_pickle=True)
epoch_save = {a: np.array(iter_save[a]) /
np.float(niter_per_epoch[a]) for a in all_algs}
out = misc.resort_out(out, obj_opt)
fig = []
algs = ['PDHG1', 'SPDHG2-21-bal', 'SPDHG2-100-bal', 'SPDHG2-252-bal']
labels = ['PDHG', 'SPDHG+ (21 subsets)', 'SPDHG+ (100)', 'SPDHG+ (252)']
lstyles = ['--'] + 3 * ['-']
misc.plots_paper(fig, [15, 40], [20, 30, 40], epoch_save, out,
lstyles, algs, misc.colors_sequ(), labels, 1.1, 4)
for fmt in formats:
for i, fi in enumerate(fig):
fi.savefig('{}/{}_{}_{}_out{}.{}'.format(folder_out, fmt,
filename, dataset, i, fmt), bbox_inches='tight')
plt.close(fi)
#%%
filename = 'map_dtv'
all_algs = ['PDHG1', 'SPDHG2-21-bal', 'SPDHG2-100-bal', 'SPDHG2-252-bal']
nepoch = 30
dataset = 'amyloid10min'
alpha = 10
eta = 1e-4
folder_main = '{}/{}_{}'.format(folder_in, filename, dataset)
folder_param = '{}/alpha{:.2g}_eta{:.2g}'.format(folder_main, alpha, eta)
folder_today = '{}/nepochs{}'.format(folder_param, nepoch)
file_target = '{}/target.npy'.format(folder_param)
x_opt, obj_opt = np.load(file_target, allow_pickle=True)
iter_save, niter, niter_per_epoch, image, out, nsub, prob = (
{}, {}, {}, {}, {}, {}, {})
for a in all_algs:
(iter_save[a], niter[a], niter_per_epoch[a], image[a], out[a],
nsub[a], prob[a]) = np.load('{}/npy/{}.npy'.format(folder_today, a),
allow_pickle=True)
epoch_save = {a: np.array(iter_save[a]) /
np.float(niter_per_epoch[a]) for a in all_algs}
out = misc.resort_out(out, obj_opt)
fig = []
algs = ['PDHG1', 'SPDHG2-21-bal', 'SPDHG2-100-bal', 'SPDHG2-252-bal']
labels = ['PDHG', 'SPDHG+ (21 subsets)', 'SPDHG+ (100)', 'SPDHG+ (252)']
lstyles = ['--'] + 3 * ['-']
misc.plots_paper(fig, [19, 47], [20, 30, 40], epoch_save, out,
lstyles, algs, misc.colors_sequ(), labels, 1.1, 4)
for fmt in formats:
for i, fi in enumerate(fig):
fi.savefig('{}/{}_{}_{}_out{}.{}'.format(folder_out, fmt,
filename, dataset, i, fmt), bbox_inches='tight')
plt.close(fi)
#%%
filename = 'map_tgv'
all_algs = ['PDHG1', 'SPDHG2-21-bal', 'SPDHG2-100-bal', 'SPDHG2-252-bal']
nepoch = 30
dataset = 'fdg'
alpha = 2
beta = .5
folder_main = '{}/{}_{}'.format(folder_in, filename, dataset)
folder_param = '{}/alpha{:.2g}_beta{:.2g}'.format(folder_main, alpha, beta)
folder_today = '{}/nepochs{}'.format(folder_param, nepoch)
file_target = '{}/target.npy'.format(folder_param)
x_opt, obj_opt = np.load(file_target, allow_pickle=True)
iter_save, niter, niter_per_epoch, image, out, nsub, prob = (
{}, {}, {}, {}, {}, {}, {})
for a in all_algs:
(iter_save[a], niter[a], niter_per_epoch[a], image[a], out[a],
nsub[a], prob[a]) = np.load('{}/npy/{}.npy'.format(folder_today, a),
allow_pickle=True)
epoch_save = {a: np.array(iter_save[a]) /
np.float(niter_per_epoch[a]) for a in all_algs}
out = misc.resort_out(out, obj_opt)
fig = []
algs = ['PDHG1', 'SPDHG2-21-bal', 'SPDHG2-100-bal', 'SPDHG2-252-bal']
labels = ['PDHG', 'SPDHG+ (21 subsets)', 'SPDHG+ (100)', 'SPDHG+ (252)']
lstyles = ['--'] + 3 * ['-']
misc.plots_paper(fig, [19, 61], [20, 30, 40, 50, 60], epoch_save, out,
lstyles, algs, misc.colors_sequ(), labels, 1.1, 4)
for fmt in formats:
for i, fi in enumerate(fig):
fi.savefig('{}/{}_{}_{}_out{}.{}'.format(folder_out, fmt,
filename, dataset, i, fmt), bbox_inches='tight')
plt.close(fi)