waves = np.load(pwd + 'wf{}_{}.npy'.format(wf, scid))
plt.plot(waves[1000])
plt.show()
# =============================================================================
# Fit-Transforming PCA on acquired waves:
fitted = utils.pca_projections(waves, 3, svd_solver='arpack')
print('Fitted PCA: {}'.format(fitted.shape))
xs = fitted[:, 0]
ys = fitted[:, 1]
zs = fitted[:, 2]
utils.plot_3d_mod(xs,
ys,
zs,
title='wf{} {}'.format(wf, scid),
s=5,
edge='navy',
xlabel='PC1',
ylabel='PC2',
zlabel='PC3',
add_info='PAT{} ({})'.format(wf, scid))
if input('Satisfied? (y|N)\n') != 'y':
raise Exception('Not satisified :(')
# =============================================================================
# Gaussian Mixture Models and, if needed, additional clustering
n_components = int(input('Type the number of components to split into: '))
fitted_copy = fitted
cluster_means, clusters, ns, ks, clusters_list = utils.gaussian_mixture_pca_projections(
fitted,
waves,
pwd = '/home/ov/data/stacked/'
# =============================================================================
# Load waves:
#stacked_waves = np.load(pwd_stacked_out)
stacked_waves = utils.load_waves_by_pat_scid([patient_nr], scid, pwd)
waves = utils.acces_waves_by_type(None, stacked_waves, wave_type)
print('Stacked size: {}'.format(stacked_waves.shape))
print('Unstacked {} waves: {}'.format(wave_type, waves.shape))
# =============================================================================
# Fit-Transforming PCA on acquired waves:
fitted = utils.pca_projections(waves, 3, svd_solver='arpack')
print('Fitted PCA: {}'.format(fitted.shape))
xs = fitted[:, 0]
ys = fitted[:, 1]
zs = fitted[:, 2]
"""
utils.plot_3d_mod(xs, ys, zs,
title='',
s=1,
edge = 'navy',
xlabel = 'PC1',
ylabel = 'PC2',
zlabel = 'PC3',
add_info = 'PAT{} ({})'.format(patient_nr, scid))
if input('Satisfied? (y|N)\n') != 'y':
raise Exception('Not satisified :(')
"""
# =============================================================================
# Gaussian Mixture Models and, if needed, additional clustering
n_components = int(input('Type the number of components to split into: '))
#pwd_stacked = pwd + 'nfpat' + str(patid) + scid + '.stacked_icp_abp.npy'
#stacked_by_pat = np.load(pwd_stacked)
stacked_by_pat = load_waves_by_pat_scid([patid], scid, pwd)
waves_by_pat = utils.acces_waves_by_type(None, stacked_by_pat, wave_type)
fitted = utils.pca_projections(waves_by_pat, 3, svd_solver='arpack')
print('Fitted PCA: {}'.format(fitted.shape))
xs = fitted[:, 0]
ys = fitted[:, 1]
zs = fitted[:, 2]
fig, ax = utils.plot_3d_mod(
xs,
ys,
zs,
title='',
s=1,
edge='navy', #utils.default_colors[i],
fig=fig,
ax=ax,
xlabel='PC1',
ylabel='PC2',
zlabel='PC3',
add_info='PAT{} ({})'.format(patid, scid),
pause_time=0.1)
i += 1
plt.suptitle('PCA projection of entire {} data'.format(scid))
plt.show(block=False)
plt.pause(0.001)
"""
i = 0
pwd = pwd_stacked
face = 'none'
for patid in patids:
wf_waves = utils.load_waves(pwd + 'wf{}_{}.npy'.format(wf, scid),
wf_waves)
fitted = utils.pca_projections(wf_waves, 3, svd_solver='arpack')
xs_list.append(fitted[:, 0])
ys_list.append(fitted[:, 1])
zs_list.append(fitted[:, 2])
# Test:
assert wf_types.shape[0] == waves.shape[0]
from sklearn.manifold import TSNE
embedded_waves = TSNE(n_components=3, verbose=1).fit_transform(waves)
embedded_waves.shape
fitted = embedded_waves
utils.plot_3d_mod(fitted[:, 0], fitted[:, 1], fitted[:, 2])
# =============================================================================
# Plot PCA proj. of ICP corresponding to each wf:
title = 'Projection of ICP waves corresponding to all {} wfs'.format(scid)
utils.stack_plot(xs_list, ys_list, zs_list, label_prefix='wf', title=title)
# =============================================================================
# Fit PCA and plot by scid
fitted = utils.pca_projections(waves, 3, svd_solver='arpack')
print('Fitted PCA: {}'.format(fitted.shape))
xs = fitted[:, 0]
ys = fitted[:, 1]
zs = fitted[:, 2]
figu, axu = utils.plot_3d_mod(
xs,
ys,
zs,
waves = utils.acces_waves_by_type(None, stacked_waves, wave_type)
print('Stacked size: {}'.format(stacked_waves.shape))
print('Unstacked {} waves: {}'.format(wave_type, waves.shape))
# =============================================================================
# Fit-Transforming PCA on acquired waves:
fitted = utils.pca_projections(waves, 3, svd_solver='arpack')
print('Fitted PCA: {}'.format(fitted.shape))
xs = fitted[:, 0]
ys = fitted[:, 1]
zs = fitted[:, 2]
utils.plot_3d_mod(xs, ys, zs,
title='PCA projection of {} waves of PAT{} ({})'.format(wave_type,
patient_nr,
scid),
s=1,
edge = 'navy',
xlabel = 'PC1',
ylabel = 'PC2',
zlabel = 'PC3',
add_info = 'PAT{} ({})'.format(patient_nr, scid))
if input('\nContinue to clustering? (y|N)\n') != 'y':
raise Exception('Not satisified :(')
# =============================================================================
# Gaussian Mixture Models and, if needed, additional clustering
n_components = int(input('Type the number of components to split into: '))
cluster_means, clusters, ns, ks, clusters_list = utils.gaussian_mixture_pca_projections(
fitted, waves, n_components,
npat=patient_nr, scid=scid,
wave_type=wave_type)
# =============================================================================
# Plot PCA proj. of ICP corresponding to each wf:
title = 'Projection of ICP waves corresponding to all wfs'
utils.stack_plot(xs_list, ys_list, zs_list, label_prefix='scid', title=title)
# =============================================================================
# Fit PCA and plot
fitted = utils.pca_projections(waves, 3, svd_solver='arpack')
print('Fitted PCA: {}'.format(fitted.shape))
xs = fitted[:, 0]
ys = fitted[:, 1]
zs = fitted[:, 2]
figu, axu = utils.plot_3d_mod(xs,
ys,
zs,
title='all icp waves from selected wfs',
s=10,
edge='navy',
xlabel='PC1',
ylabel='PC2',
zlabel='PC3',
add_info='({})'.format(scid))
if input('\nSatisfied? (y|N)\n') != 'y':
raise Exception('Not satisified :(')
# =============================================================================
# Gaussian Mixture Models OR HDBSCAN and, if needed, additional clustering *
decision = input('dbscan or gmm: ')
if decision == 'gmm':
n_components = int(input('Type the number of components to split into: '))
cluster_means, clusters, ns, ks, clusters_list = utils.gaussian_mixture_pca_projections(
