def generate_figure(R, MU, n_src, true_csd_xlims, total_ele,
method='cross-validation', Rs=None, lambdas=None,
noise=0):
"""
Generates figure for targeted basis investigation.
Parameters
----------
R: float
Thickness of the groundtruth source.
Default: 0.2.
MU: float
Central position of Gaussian source
Default: 0.25.
nr_src: int
Number of basis sources.
true_csd_xlims: list
Boundaries for ground truth space.
total_ele: int
Number of electrodes.
save_path: string
Directory.
method: string
Determines the method of regularization.
Default: cross-validation.
Rs: numpy 1D array
Basis source parameter for crossvalidation.
Default: None.
lambdas: numpy 1D array
Regularization parameter for crossvalidation.
Default: None.
noise: float
Determines the level of noise in the data.
Default: 0.
Returns
-------
None
"""
ele_lims = [0, 1.]
csd_at, true_csd, ele_pos, pots, val = tb.simulate_data(tb.csd_profile,
true_csd_xlims, R,
MU, total_ele,
ele_lims,
noise=noise)
fig = plt.figure(figsize=(15, 12))
widths = [1, 1, 1]
heights = [1, 1, 1]
gs = gridspec.GridSpec(3, 3, height_ratios=heights, width_ratios=widths,
hspace=0.45, wspace=0.3)
ax = fig.add_subplot(gs[0, 0])
xmin = 0
xmax = 1
ext_x = 0
obj = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=method, Rs=Rs, lambdas=lambdas)
make_subplot(ax, true_csd, obj.values('CSD'), obj.estm_x, ele_pos=ele_pos,
title='Basis limits = [0, 1]', xlabel=False, ylabel=True,
letter='A')
ax = fig.add_subplot(gs[0, 1])
xmin = -0.5
xmax = 1
ext_x = -0.5
obj = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=method, Rs=Rs, lambdas=lambdas)
make_subplot(ax, true_csd, obj.values('CSD'), obj.estm_x, ele_pos=ele_pos,
title='Basis limits = [0, 0.5]', xlabel=False, ylabel=False,
letter='B')
ax = fig.add_subplot(gs[0, 2])
xmin = 0
xmax = 1.5
ext_x = -0.5
obj = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=method, Rs=Rs, lambdas=lambdas)
make_subplot(ax, true_csd, obj.values('CSD'), obj.estm_x, ele_pos=ele_pos,
title='Basis limits = [0.5, 1]', xlabel=False, ylabel=False,
letter='C')
ele_lims = [0, 0.5]
# total_ele = 6
csd_at, true_csd, ele_pos, pots, val = tb.simulate_data(tb.csd_profile,
true_csd_xlims, R,
MU, total_ele,
ele_lims,
noise=noise)
ax = fig.add_subplot(gs[1, 0])
xmin = 0
xmax = 1
ext_x = 0
obj = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=method, Rs=Rs, lambdas=lambdas)
make_subplot(ax, true_csd, obj.values('CSD'), obj.estm_x, ele_pos=ele_pos,
title=None, xlabel=False, ylabel=True, letter='D')
ax = fig.add_subplot(gs[1, 1])
xmin = -0.5
xmax = 1
ext_x = -0.5
obj = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=method, Rs=Rs, lambdas=lambdas)
make_subplot(ax, true_csd, obj.values('CSD'), obj.estm_x, ele_pos=ele_pos,
title=None, xlabel=False, ylabel=False, letter='E')
ax = fig.add_subplot(gs[1, 2])
xmin = 0
xmax = 1.5
ext_x = -0.5
obj = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=method, Rs=Rs, lambdas=lambdas)
make_subplot(ax, true_csd, obj.values('CSD'), obj.estm_x, ele_pos=ele_pos,
title=None, xlabel=False, ylabel=False, letter='F')
ele_lims = [0.5, 1.]
csd_at, true_csd, ele_pos, pots, val = tb.simulate_data(tb.csd_profile,
true_csd_xlims, R,
MU, total_ele,
ele_lims,
noise=noise)
ax = fig.add_subplot(gs[2, 0])
xmin = 0
xmax = 1
ext_x = 0
obj = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=method, Rs=Rs, lambdas=lambdas)
make_subplot(ax, true_csd, obj.values('CSD'), obj.estm_x, ele_pos=ele_pos,
title=None, xlabel=True, ylabel=True, letter='G')
ax = fig.add_subplot(gs[2, 1])
xmin = -0.5
xmax = 1
ext_x = -0.5
obj = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=method, Rs=Rs, lambdas=lambdas)
make_subplot(ax, true_csd, obj.values('CSD'), obj.estm_x, ele_pos=ele_pos,
title=None, xlabel=True, ylabel=False, letter='H')
ax = fig.add_subplot(gs[2, 2])
xmin = 0
xmax = 1.5
ext_x = -0.5
obj = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=method, Rs=Rs, lambdas=lambdas)
ax = make_subplot(ax, true_csd, obj.values('CSD'), obj.estm_x,
ele_pos=ele_pos, title=None, xlabel=True, ylabel=False,
letter='I')
handles, labels = ax.get_legend_handles_labels()
fig.legend(handles, labels, loc='lower center', ncol=3, frameon=False)
fig.savefig(os.path.join('targeted_basis_' + method +
'_noise_' + str(noise) + '.png'), dpi=300)
plt.show()
def generate_figure_CVLC(R, MU, n_src, true_csd_xlims, total_ele, save_path,
Rs=None, lambdas=None, noise=0):
"""
Generates figure for targeted basis investigation including results from
both cross-validation and L-curve.
Parameters
----------
R: float
Thickness of the groundtruth source.
Default: 0.2.
MU: float
Central position of Gaussian source
Default: 0.25.
nr_src: int
Number of basis sources.
true_csd_xlims: list
Boundaries for ground truth space.
total_ele: int
Number of electrodes.
save_path: string
Directory.
Rs: numpy 1D array
Basis source parameter for crossvalidation.
Default: None.
lambdas: numpy 1D array
Regularization parameter for crossvalidation.
Default: None.
noise: float
Determines the level of noise in the data.
Default: 0.
Returns
-------
None
"""
m_cv = 'cross-validation'
m_lc = 'L-curve'
method = 'CV_LC'
ele_lims = [0, 1.]
csd_at, true_csd, ele_pos, pots, val = tb.simulate_data(tb.csd_profile,
true_csd_xlims, R,
MU, total_ele,
ele_lims,
noise=noise)
fig = plt.figure(figsize=(15, 12))
widths = [1, 1, 1]
heights = [1, 1, 1]
gs = gridspec.GridSpec(3, 3, height_ratios=heights, width_ratios=widths,
hspace=0.45, wspace=0.3)
ax = fig.add_subplot(gs[0, 0])
xmin = 0
xmax = 1
ext_x = 0
obj_CV = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=m_cv, Rs=Rs, lambdas=lambdas)
obj_LC = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=m_lc, Rs=Rs, lambdas=lambdas)
make_subplot(ax, true_csd, obj_CV.values('CSD'), obj_CV.estm_x,
ele_pos=ele_pos, title='Basis limits = [0, 1]', xlabel=False,
ylabel=True, letter='A', est_csd_LC=obj_LC.values('CSD'))
ax = fig.add_subplot(gs[0, 1])
xmin = -0.5
xmax = 1
ext_x = -0.5
obj_CV = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=m_cv, Rs=Rs, lambdas=lambdas)
obj_LC = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=m_lc, Rs=Rs, lambdas=lambdas)
make_subplot(ax, true_csd, obj_CV.values('CSD'), obj_CV.estm_x,
ele_pos=ele_pos, title='Basis limits = [0, 0.5]',
xlabel=False, ylabel=False, letter='B',
est_csd_LC=obj_LC.values('CSD'))
ax = fig.add_subplot(gs[0, 2])
xmin = 0
xmax = 1.5
ext_x = -0.5
obj_CV = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=m_cv, Rs=Rs, lambdas=lambdas)
obj_LC = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=m_lc, Rs=Rs, lambdas=lambdas)
make_subplot(ax, true_csd, obj_CV.values('CSD'), obj_CV.estm_x,
ele_pos=ele_pos, title='Basis limits = [0.5, 1]',
xlabel=False, ylabel=False, letter='C',
est_csd_LC=obj_LC.values('CSD'))
ele_lims = [0, 0.5]
# total_ele = 6
csd_at, true_csd, ele_pos, pots, val = tb.simulate_data(tb.csd_profile,
true_csd_xlims, R,
MU, total_ele,
ele_lims,
noise=noise)
ax = fig.add_subplot(gs[1, 0])
xmin = 0
xmax = 1
ext_x = 0
obj_CV = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=m_cv, Rs=Rs, lambdas=lambdas)
obj_LC = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=m_lc, Rs=Rs, lambdas=lambdas)
make_subplot(ax, true_csd, obj_CV.values('CSD'), obj_CV.estm_x,
ele_pos=ele_pos, title=None, xlabel=False, ylabel=True,
letter='D', est_csd_LC=obj_LC.values('CSD'))
ax = fig.add_subplot(gs[1, 1])
xmin = -0.5
xmax = 1
ext_x = -0.5
obj_CV = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=m_cv, Rs=Rs, lambdas=lambdas)
obj_LC = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=m_lc, Rs=Rs, lambdas=lambdas)
make_subplot(ax, true_csd, obj_CV.values('CSD'), obj_CV.estm_x,
ele_pos=ele_pos, title=None, xlabel=False, ylabel=False,
letter='E', est_csd_LC=obj_LC.values('CSD'))
ax = fig.add_subplot(gs[1, 2])
xmin = 0
xmax = 1.5
ext_x = -0.5
obj_CV = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=m_cv, Rs=Rs, lambdas=lambdas)
obj_LC = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=m_lc, Rs=Rs, lambdas=lambdas)
make_subplot(ax, true_csd, obj_CV.values('CSD'), obj_CV.estm_x,
ele_pos=ele_pos, title=None, xlabel=False, ylabel=False,
letter='F', est_csd_LC=obj_LC.values('CSD'))
ele_lims = [0.5, 1.]
csd_at, true_csd, ele_pos, pots, val = tb.simulate_data(tb.csd_profile,
true_csd_xlims, R,
MU, total_ele,
ele_lims,
noise=noise)
ax = fig.add_subplot(gs[2, 0])
xmin = 0
xmax = 1
ext_x = 0
obj_CV = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=m_cv, Rs=Rs, lambdas=lambdas)
obj_LC = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=m_lc, Rs=Rs, lambdas=lambdas)
make_subplot(ax, true_csd, obj_CV.values('CSD'), obj_CV.estm_x,
ele_pos=ele_pos, title=None, xlabel=True, ylabel=True,
letter='G', est_csd_LC=obj_LC.values('CSD'))
ax = fig.add_subplot(gs[2, 1])
xmin = -0.5
xmax = 1
ext_x = -0.5
obj_CV = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=m_cv, Rs=Rs, lambdas=lambdas)
obj_LC = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=m_lc, Rs=Rs, lambdas=lambdas)
make_subplot(ax, true_csd, obj_CV.values('CSD'), obj_CV.estm_x,
ele_pos=ele_pos, title=None, xlabel=True, ylabel=False,
letter='H', est_csd_LC=obj_LC.values('CSD'))
ax = fig.add_subplot(gs[2, 2])
xmin = 0
xmax = 1.5
ext_x = -0.5
obj_CV = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=m_cv, Rs=Rs, lambdas=lambdas)
obj_LC = tb.modified_bases(val, pots, ele_pos, n_src, h=0.25,
sigma=0.3, gdx=0.01, ext_x=ext_x, xmin=xmin,
xmax=xmax, method=m_lc, Rs=Rs, lambdas=lambdas)
ax = make_subplot(ax, true_csd, obj_CV.values('CSD'), obj_CV.estm_x,
ele_pos=ele_pos, title=None, xlabel=True, ylabel=False,
letter='I', est_csd_LC=obj_LC.values('CSD'))
handles, labels = ax.get_legend_handles_labels()
fig.legend(handles, labels, loc='lower center', ncol=4, frameon=False)
fig.savefig(os.path.join(save_path, 'targeted_basis_' + method +
'_noise_' + str(noise) + '.png'), dpi=300)
plt.show()
def generate_figure(csd_profile,
R,
MU,
true_csd_xlims,
total_ele,
ele_lims,
save_path,
noise=0):
"""
Generates figure for spectral structure decomposition.
Parameters
----------
csd_profile: function
Function to produce csd profile.
R: float
Thickness of the groundtruth source.
Default: 0.2.
MU: float
Central position of Gaussian source
Default: 0.25.
true_csd_xlims: list
Boundaries for ground truth space.
total_ele: int
Number of electrodes.
ele_lims: list
Electrodes limits.
save_path: string
Directory.
method: string
Determines the method of regularization.
Default: cross-validation.
Rs: numpy 1D array
Basis source parameter for crossvalidation.
Default: None.
lambdas: numpy 1D array
Regularization parameter for crossvalidation.
Default: None.
noise: float
Determines the level of noise in the data.
Default: 0.
Returns
-------
None
"""
csd_at, true_csd, ele_pos, pots, val = tb.simulate_data(csd_profile,
true_csd_xlims,
R,
MU,
total_ele,
ele_lims,
noise=noise)
n_src_M = [512]
R_init = [0.05, 0.1, 0.2, 0.4, 0.8]
OBJ_M, eigenval_M, eigenvec_M = stability_M(csd_profile,
n_src_M,
ele_lims,
true_csd_xlims,
total_ele,
ele_pos,
pots,
R_init=R_init)
plt_cord = [(2, 0), (2, 2), (2, 4), (3, 0), (3, 2), (3, 4), (4, 0), (4, 2),
(4, 4), (5, 0), (5, 2), (5, 4)]
letters = ['C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N']
BLACK = _html(0, 0, 0)
ORANGE = _html(230, 159, 0)
SKY_BLUE = _html(86, 180, 233)
GREEN = _html(0, 158, 115)
YELLOW = _html(240, 228, 66)
BLUE = _html(0, 114, 178)
VERMILION = _html(213, 94, 0)
PURPLE = _html(204, 121, 167)
colors = [BLUE, ORANGE, GREEN, PURPLE, VERMILION, SKY_BLUE, YELLOW, BLACK]
fig = plt.figure(figsize=(18, 16))
heights = [4, 0.3, 1, 1, 1, 1]
markers = ['^', '.', '*', 'x', ',']
linestyles = ['-', '-', '-', '-', '-']
gs = gridspec.GridSpec(6, 6, height_ratios=heights, hspace=0.3, wspace=0.6)
ax = fig.add_subplot(gs[0, :3])
for indx, i in enumerate(R_init):
ax.plot(np.arange(1, total_ele + 1),
eigenval_M[indx],
linestyle=linestyles[indx],
color=colors[indx],
marker=markers[indx],
label='R=' + str(R_init[indx]),
markersize=10)
ht, lh = ax.get_legend_handles_labels()
set_axis(ax, -0.05, 1.05, letter='A')
ax.set_xlabel('Number of components')
ax.set_ylabel('Eigenvalues')
ax.set_yscale('log')
ax.set_ylim([1e-6, 1])
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax = fig.add_subplot(gs[0, 3:])
ax.plot(R_init,
eigenval_M[:, 0],
marker='s',
color='k',
markersize=5,
linestyle=' ')
set_axis(ax, -0.05, 1.05, letter='B')
ax.set_xlabel('R')
ax.set_ylabel('Eigenvalues')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
for i in range(OBJ_M[0].k_interp_cross.shape[1]):
ax = fig.add_subplot(gs[plt_cord[i][0],
plt_cord[i][1]:plt_cord[i][1] + 2])
for idx, j in enumerate(R_init):
a = np.dot(OBJ_M[idx].k_interp_cross, eigenvec_M[idx, :, i])
a = a / np.linalg.norm(a)
ax.plot(np.linspace(0, 1, 100),
a,
linestyle=linestyles[idx],
color=colors[idx],
label='M=' + str(R_init[idx]),
lw=2)
ax.text(0.5,
1.,
r"$\tilde{K}\cdot{v_{{%(i)d}}}$" % {'i': i + 1},
horizontalalignment='center',
transform=ax.transAxes,
fontsize=20)
set_axis(ax, -0.10, 1.1, letter=letters[i])
if i < 9:
ax.get_xaxis().set_visible(False)
ax.spines['bottom'].set_visible(False)
else:
ax.set_xlabel('Depth ($mm$)')
if i % 3 == 0:
ax.set_ylabel('CSD ($mA/mm$)')
ax.yaxis.set_label_coords(-0.18, 0.5)
ax.ticklabel_format(style='sci', axis='y', scilimits=((0.0, 0.0)))
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
fig.legend(ht, lh, loc='lower center', ncol=5, frameon=False)
fig.savefig(os.path.join(
save_path, 'vectors_' + '_noise_' + str(noise) + 'R0_8' + '.png'),
dpi=300)
plt.show()
def generate_figure_projection(R, MU, n_src, true_csd_xlims, total_ele,
method='cross-validation', Rs=None, lambdas=None,
noise=0):
"""
Generates figure for targeted basis investigation.
Parameters
----------
R: float
Thickness of the groundtruth source.
Default: 0.2.
MU: float
Central position of Gaussian source
Default: 0.25.
nr_src: int
Number of basis sources.
true_csd_xlims: list
Boundaries for ground truth space.
total_ele: int
Number of electrodes.
save_path: string
Directory.
method: string
Determines the method of regularization.
Default: cross-validation.
Rs: numpy 1D array
Basis source parameter for crossvalidation.
Default: None.
lambdas: numpy 1D array
Regularization parameter for crossvalidation.
Default: None.
noise: float
Determines the level of noise in the data.
Default: 0.
Returns
-------
None
"""
true_csd_projection = tb.csd_profile(np.linspace(0, 1, 100), [R, MU])
ele_lims = [0, 0.5]
csd_at, true_csd, ele_pos, pots, val = tb.simulate_data(tb.csd_profile,
true_csd_xlims, R,
MU, total_ele,
ele_lims,
noise=noise)
fig = plt.figure(figsize=(15, 12))
widths = [1, 1, 1, 1]
heights = [1, 1, 1]
gs = gridspec.GridSpec(3, 4, height_ratios=heights, width_ratios=widths,
hspace=0.45, wspace=0.3)
#estimation [0, 1], sources[0, 0.5]
own_est = np.linspace(0, 1, 100)
own_src = np.linspace(0, 0.5, n_src)
obj = modified_bases(val, pots, ele_pos, n_src, own_est, own_src, h=0.25,
sigma=0.3, method=method, Rs=Rs, lambdas=lambdas)
ax = fig.add_subplot(gs[0, 0])
fb.make_subplot(ax, true_csd, obj.values('CSD'), obj.estm_x, ele_pos=ele_pos,
title="CSD", xlabel=False, ylabel=True, letter='A')
eigensources = calculate_eigensources(obj)
projection = csd_into_eigensource_projection(true_csd_projection, eigensources)
anihilator = true_csd_projection - projection
ax = fig.add_subplot(gs[0, 1])
plot_projection(ax, projection, obj.estm_x, ele_pos=ele_pos,
title='Projection', xlabel=False, ylabel=False, letter='B')
ax = fig.add_subplot(gs[0, 2])
plot_projection(ax, anihilator, obj.estm_x, ele_pos=ele_pos,
title='Annihilated', xlabel=False, ylabel=False, letter='C', c='r', label='Annihilated')
ax = fig.add_subplot(gs[0, 3])
make_subplot(ax, eigensources, obj.estm_x, ele_pos=ele_pos,
title='Eigensources', xlabel=False, ylabel=False, letter='D')
ele_lims = [0.5, 1]
csd_at, true_csd, ele_pos, pots, val = tb.simulate_data(tb.csd_profile,
true_csd_xlims, R,
MU, total_ele,
ele_lims,
noise=noise)
#estimation [0, 1], sources[0, 0.5]
own_est = np.linspace(0., 1., 100)
own_src = np.linspace(0., 0.5, n_src)
obj = modified_bases(val, pots, ele_pos, n_src, own_est, own_src, h=0.25,
sigma=0.3, method=method, Rs=Rs, lambdas=lambdas)
ax = fig.add_subplot(gs[1, 0])
fb.make_subplot(ax, true_csd, obj.values('CSD'), obj.estm_x, ele_pos=ele_pos,
title=None, xlabel=False, ylabel=True, letter='E')
eigensources = calculate_eigensources(obj)
projection = csd_into_eigensource_projection(true_csd_projection, eigensources)
anihilator = true_csd_projection - projection
ax = fig.add_subplot(gs[1, 1])
plot_projection(ax, projection, obj.estm_x, ele_pos=ele_pos,
title=None, xlabel=False, ylabel=False, letter='F')
ax = fig.add_subplot(gs[1, 2])
plot_projection(ax, anihilator, obj.estm_x, ele_pos=ele_pos,
title=None, xlabel=False, ylabel=False, letter='G', c='r')
ax = fig.add_subplot(gs[1, 3])
make_subplot(ax, eigensources, obj.estm_x, ele_pos=ele_pos,
title=None, xlabel=False, ylabel=False, letter='H')
#estimation [0, 1], sources[0.5, 1]
own_est3 = np.linspace(0., 1., 100)
own_src3 = np.linspace(0.5, 1., n_src)
obj = modified_bases(val, pots, ele_pos, n_src, own_est3, own_src3, h=0.25,
sigma=0.3, method=method, Rs=Rs, lambdas=lambdas)
ax1 = fig.add_subplot(gs[2, 0])
ax1 = fb.make_subplot(ax1, true_csd, obj.values('CSD'), obj.estm_x, ele_pos=ele_pos,
title=None, xlabel=True, ylabel=True, letter='I')
eigensources = calculate_eigensources(obj)
projection = csd_into_eigensource_projection(true_csd_projection, eigensources)
anihilator = true_csd_projection - projection
ax2 = fig.add_subplot(gs[2, 1])
ax2 = plot_projection(ax2, projection, obj.estm_x, ele_pos=ele_pos,
title=None, xlabel=True, ylabel=False, letter='J')
ax3 = fig.add_subplot(gs[2, 2])
ax3 = plot_projection(ax3, anihilator, obj.estm_x, ele_pos=ele_pos,
title=None, xlabel=True, ylabel=False, letter='K', c='r', label='Annihilated')
ax4 = fig.add_subplot(gs[2, 3])
ax4 = make_subplot(ax4, eigensources, obj.estm_x,
ele_pos=ele_pos, title=None, xlabel=True, ylabel=False,
letter='L')
handles, labels = [(a + b +c + d) for a, b, c, d in zip(ax1.get_legend_handles_labels(), ax2.get_legend_handles_labels(),
ax3.get_legend_handles_labels(), ax4.get_legend_handles_labels())]
fig.legend(handles, labels, loc='lower center', ncol=6, frameon=False)
fig.savefig(os.path.join('Projection_targeted_basis_noise_' + str(noise) + 'normalized2_oKCSD1D.png'), dpi=300)
plt.show()
def generate_figure(csd_profile, R, MU, true_csd_xlims, total_ele, ele_lims,
noise=0, R_init=0.23):
"""
Generates figure for spectral structure decomposition.
Parameters
----------
csd_profile: function
Function to produce csd profile.
R: float
Thickness of the groundtruth source.
Default: 0.2.
MU: float
Central position of Gaussian source
Default: 0.25.
true_csd_xlims: list
Boundaries for ground truth space.
total_ele: int
Number of electrodes.
ele_lims: list
Electrodes limits.
noise: float
Determines the level of noise in the data.
Default: 0.
R_init: float
Initial value of R parameter - width of basis source
Default: 0.23.
Returns
-------
None
"""
csd_at, true_csd, ele_pos, pots, val = tb.simulate_data(csd_profile,
true_csd_xlims,
R, MU, total_ele,
ele_lims,
noise=noise)
print('csd', true_csd.shape)
n_src_M = [2, 4, 8, 16, 32, 64, 128, 256, 512]
OBJ_M, eigenval_M, eigenvec_M = stability_M(n_src_M,
total_ele, ele_pos, pots,
R_init=R_init)
# print('eigenvector', eigenvec_M[0].shape)
# eigensources = calculate_eigensources(OBJ_M[0].k_interp_cross, eigenvec_M[0])
projection_M = []
fig = plt.figure(figsize=(10, 6))
ax = fig.add_subplot(131)
ax.set_title('Projection')
for i in range(len(n_src_M)):
projection = calculate_projection(true_csd, OBJ_M[i], eigenvec_M[i])
projection_M.append(projection)
ax.plot(np.linspace(0, 1, 100), projection, label='M='+str(n_src_M[i]))
ax.set_xlabel('Depth (mm)')
ax.set_ylabel('CSD (mA/mm)')
set_axis(ax, -0.05, 1.05, letter='A')
ax = fig.add_subplot(132)
ax.set_title('Error')
for i in range(len(n_src_M)):
err = calculate_diff(true_csd, projection_M[i])
ax.plot(np.linspace(0, 1, 100), err, label='M='+str(n_src_M[i]))
ax.set_xlabel('Depth (mm)')
set_axis(ax, -0.05, 1.05, letter='B')
ax = fig.add_subplot(133)
ax.set_title('Anihilator')
for i in range(len(n_src_M)):
err = calculate_diff(true_csd.reshape(true_csd.shape[0], 1), OBJ_M[i].values('CSD'))
ax.plot(np.linspace(0, 1, 100), err, label='M='+str(n_src_M[i]))
ax.set_xlabel('Depth (mm)')
set_axis(ax, -0.05, 1.05, letter='C')
plt.tight_layout()
handles, labels = ax.get_legend_handles_labels()
lgd = fig.legend(handles, labels, loc='lower center', ncol=9, frameon=False, bbox_to_anchor=(0.5, -0.02), fontsize=10)
fig.savefig(os.path.join('projections_different_M' + '.png'), dpi=300, bbox_extra_artists=(lgd,), bbox_inches='tight')
print(OBJ_M[i].values('CSD').shape)
# BLACK = _html(0, 0, 0)
# ORANGE = _html(230, 159, 0)
# SKY_BLUE = _html(86, 180, 233)
# GREEN = _html(0, 158, 115)
# YELLOW = _html(240, 228, 66)
# BLUE = _html(0, 114, 178)
# VERMILION = _html(213, 94, 0)
# PURPLE = _html(204, 121, 167)
# colors = [BLUE, ORANGE, GREEN, PURPLE, VERMILION, SKY_BLUE, YELLOW, BLACK]
# fig = plt.figure(figsize=(18, 16))
# # heights = [1, 1, 1, 0.2, 1, 1, 1, 1]
# heights = [4, 0.3, 1, 1, 1, 1]
# markers = ['^', '.', '*', 'x', ',']
# # linestyles = [':', '--', '-.', '-']
# linestyles = ['-', '-', '-', '-']
return true_csd, projection_M
def generate_figure(csd_profile, R, MU, true_csd_xlims, total_ele, ele_lims,
save_path, method='cross-validation', Rs=None,
lambdas=None, noise=0):
"""
Generates figure for spectral structure decomposition.
Parameters
----------
csd_profile: function
Function to produce csd profile.
R: float
Thickness of the groundtruth source.
Default: 0.2.
MU: float
Central position of Gaussian source
Default: 0.25.
true_csd_xlims: list
Boundaries for ground truth space.
total_ele: int
Number of electrodes.
ele_lims: list
Electrodes limits.
save_path: string
Directory.
method: string
Determines the method of regularization.
Default: cross-validation.
Rs: numpy 1D array
Basis source parameter for crossvalidation.
Default: None.
lambdas: numpy 1D array
Regularization parameter for crossvalidation.
Default: None.
noise: float
Determines the level of noise in the data.
Default: 0.
Returns
-------
None
"""
csd_at, true_csd, ele_pos, pots, val = tb.simulate_data(csd_profile,
true_csd_xlims,
R, MU, total_ele,
ele_lims,
noise=noise)
n_src_M = [2, 4, 8, 16, 32, 64, 128, 256, 512]
OBJ_M, eigenval_M, eigenvec_M = stability_M(csd_profile, n_src_M,
ele_lims, true_csd_xlims,
total_ele, ele_pos, pots,
method=method, Rs=Rs,
lambdas=lambdas)
plt_cord = [(2, 0), (2, 2), (2, 4),
(3, 0), (3, 2), (3, 4),
(4, 0), (4, 2), (4, 4),
(5, 0), (5, 2), (5, 4)]
letters = ['C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'O']
BLACK = _html(0, 0, 0)
ORANGE = _html(230, 159, 0)
SKY_BLUE = _html(86, 180, 233)
GREEN = _html(0, 158, 115)
YELLOW = _html(240, 228, 66)
BLUE = _html(0, 114, 178)
VERMILION = _html(213, 94, 0)
PURPLE = _html(204, 121, 167)
colors = [BLUE, ORANGE, GREEN, PURPLE, VERMILION, SKY_BLUE, YELLOW, BLACK]
fig = plt.figure(figsize=(18, 16))
# heights = [1, 1, 1, 0.2, 1, 1, 1, 1]
heights = [4, 0.3, 1, 1, 1, 1]
markers = ['^', '.', '*', 'x', ',']
# linestyles = [':', '--', '-.', '-']
linestyles = ['-', '-', '-', '-']
src_idx = [0, 2, 3, 8]
gs = gridspec.GridSpec(6, 6, height_ratios=heights, hspace=0.3, wspace=0.6)
ax = fig.add_subplot(gs[0, :3])
for indx, i in enumerate(src_idx):
ax.plot(np.arange(1, total_ele + 1), eigenval_M[i],
linestyle=linestyles[indx], color=colors[indx],
marker=markers[indx], label='M='+str(n_src_M[i]),
markersize=10)
# ax.set_title(' ', fontsize=12)
ht, lh = ax.get_legend_handles_labels()
set_axis(ax, -0.05, 1.05, letter='A')
# ax.legend(loc='lower left')
ax.set_xlabel('Number of components')
ax.set_ylabel('Eigenvalues')
ax.set_yscale('log')
ax.set_ylim([1e-6, 1])
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax = fig.add_subplot(gs[0, 3:])
ax.plot(n_src_M, eigenval_M[:, 0], marker='s', color='k', markersize=5,
linestyle=' ')
#ax.set_title(' ', fontsize=12)
set_axis(ax, -0.05, 1.05, letter='B')
ax.set_xlabel('Number of basis sources')
ax.set_xscale('log')
ax.set_ylabel('Eigenvalues')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
for i in range(OBJ_M[0].k_interp_cross.shape[1]):
ax = fig.add_subplot(gs[plt_cord[i][0],
plt_cord[i][1]:plt_cord[i][1]+2])
for idx, j in enumerate(src_idx):
ax.plot(np.linspace(0, 1, 100), np.dot(OBJ_M[j].k_interp_cross,
eigenvec_M[j, :, i]),
linestyle=linestyles[idx], color=colors[idx],
label='M='+str(n_src_M[j]), lw=2)
#ax.set_title(r"$\tilde{K}*v_{{%(i)d}}$" % {'i': i+1})
ax.text(0.5, 1., r"$\tilde{K}*v_{{%(i)d}}$" % {'i': i+1},
horizontalalignment='center', transform=ax.transAxes, fontsize=20)
# ax.locator_params(axis='y', nbins=3)
# ax.set_xlabel('Depth (mm)', fontsize=12)
# ax.set_ylabel('CSD (mA/mm)', fontsize=12)
set_axis(ax, -0.10, 1.1, letter=letters[i])
if i < 9:
ax.get_xaxis().set_visible(False)
ax.spines['bottom'].set_visible(False)
else:
ax.set_xlabel('Depth ($mm$)')
if i % 3 == 0:
ax.set_ylabel('CSD ($mA/mm$)')
ax.yaxis.set_label_coords(-0.18, 0.5)
# ax.yaxis.get_major_formatter().set_powerlimits((0, 1))
# ax.tick_params(direction='out', pad=10)
# ax.yaxis.get_major_formatter(FormatStrFormatter('%.2f'))
ax.ticklabel_format(style='sci', axis='y', scilimits=((0.0, 0.0)))
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# ht, lh = ax.get_legend_handles_labels()
# ax = fig.add_subplot(gs[3, :])
# ax.legend(ht, lh, fancybox=False, shadow=False, ncol=len(src_idx),
# loc='upper center', frameon=False, bbox_to_anchor=(0.5, 0.0))
# ax.axis('off')
# plt.tight_layout()
fig.legend(ht, lh, loc='lower center', ncol=5, frameon=False)
fig.savefig(os.path.join(save_path, 'vectors_' + method +
'_noise_' + str(noise) + '.png'), dpi=300)
plt.show()