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)