def test_plot_cuboid_as_curve(self):
from pyhrf.ndarray import xndarray
sh = (10, 10, 5, 3)
data = np.zeros(sh)
data[:, :, :, 0] = 1.0
data[:, :, :, 1] = 2.0
data[:, :, :, 2] = 3.0
c1 = xndarray(
data,
axes_names=["sagittal", "coronal", "axial", "condition"],
axes_domains={"condition": ["audio1", "audio2", "video"]},
)
f = plt.figure()
ax = f.add_subplot(111)
ori = ["condition", "sagittal"]
plot_cub_as_curve(
c1.sub_cuboid(axial=0, coronal=0).reorient(ori),
colors={"audio1": "red", "audio2": "orange", "video": "blue"},
axes=ax,
)
if 0:
plt.show()
def test_plot_cuboid1d_as_curve(self):
from pyhrf.ndarray import xndarray
sh = (3,)
conds = np.array(['audio1', 'audio2', 'video'])
c2 = xndarray(np.arange(np.prod(sh)).reshape(sh),
axes_names=['condition'],
axes_domains={'condition': conds})
f = plt.figure()
ax = f.add_subplot(111)
plot_cub_as_curve(c2, axes=ax, show_axis_labels=True)
def test_plot_cuboid1d_as_curve(self):
from pyhrf.ndarray import xndarray
sh = (3,)
conds = np.array(["audio1", "audio2", "video"])
c2 = xndarray(np.arange(np.prod(sh)).reshape(sh), axes_names=["condition"], axes_domains={"condition": conds})
f = plt.figure()
ax = f.add_subplot(111)
plot_cub_as_curve(c2, axes=ax, show_axis_labels=True)
if 0:
plt.show()
def test_plot_cuboid_as_curve(self):
from pyhrf.ndarray import xndarray
sh = (10, 10, 5, 3)
data = np.zeros(sh)
data[:, :, :, 0] = 1.
data[:, :, :, 1] = 2.
data[:, :, :, 2] = 3.
c1 = xndarray(data, axes_names=['sagittal', 'coronal', 'axial', 'condition'],
axes_domains={'condition': ['audio1', 'audio2', 'video']})
f = plt.figure()
ax = f.add_subplot(111)
ori = ['condition', 'sagittal']
plot_cub_as_curve(c1.sub_cuboid(axial=0, coronal=0).reorient(ori),
colors={'audio1': 'red', 'audio2': 'orange',
'video': 'blue'}, axes=ax)
def plot_estimation_results(fig_dir, poi, jde_roi, cond, plot_label,
glm_fir_output_dir, rfir_output_dir,
jde_output_dir, ymin=-1.55, ymax=1.05,
plot_fontsize=25):
## HRF plots
fn = op.join(glm_fir_output_dir, 'glm_fir_hrf.nii.gz')
fir = xndarray.load(fn).sub_cuboid(condition=cond, **poi)
#fir /= (fir**2).sum()**.5
fir /= fir.max()
fn = op.join(rfir_output_dir, 'rfir_ehrf.nii.gz')
rfir = xndarray.load(fn).sub_cuboid(condition=cond, **poi)
#rfir /= (rfir**2).sum()**.5
rfir /= rfir.max()
fn = op.join(jde_output_dir, 'jde_mcmc_hrf_pm.nii.gz')
jde = xndarray.load(fn).sub_cuboid(ROI=jde_roi)
jde /= jde.max()
plt.figure()
pargs = {'linewidth' : 2.7}
plot_cub_as_curve(fir, show_axis_labels=False, plot_kwargs=pargs)
plot_cub_as_curve(rfir, show_axis_labels=False, plot_kwargs=pargs)
plot_cub_as_curve(jde, show_axis_labels=False, plot_kwargs=pargs)
from pyhrf.boldsynth.hrf import getCanoHRF
time_points, hcano = getCanoHRF()
hcano /= hcano.max()
plt.plot(time_points, hcano, 'k.-',linewidth=1.5)
set_ticks_fontsize(plot_fontsize)
plt.xlim(0,25)
plt.ylim(ymin, ymax)
plt.gca().xaxis.grid(True, 'major', linestyle='--', linewidth=1.2,
color='gray')
hrf_fig_fn = op.join(fig_dir, 'real_data_hrfs_%s.png' %plot_label)
print 'hrf_fig_fn:', hrf_fig_fn
plt.savefig(hrf_fig_fn)
autocrop(hrf_fig_fn)
