def test_adc():
"""
Test the implementation of the calculation of apparent diffusion
coefficient
"""
data, gtab = dsi_voxels()
dm = dti.TensorModel(gtab, 'LS')
mask = np.zeros(data.shape[:-1], dtype=bool)
mask[0, 0, 0] = True
dtifit = dm.fit(data)
# The ADC in the principal diffusion direction should be equal to the AD in
# each voxel:
pdd0 = dtifit.evecs[0, 0, 0, 0]
sphere_pdd0 = dps.Sphere(x=pdd0[0], y=pdd0[1], z=pdd0[2])
npt.assert_array_almost_equal(dtifit.adc(sphere_pdd0)[0, 0, 0],
dtifit.ad[0, 0, 0],
decimal=5)
# Test that it works for cases in which the data is 1D
dtifit = dm.fit(data[0, 0, 0])
sphere_pdd0 = dps.Sphere(x=pdd0[0], y=pdd0[1], z=pdd0[2])
npt.assert_array_almost_equal(dtifit.adc(sphere_pdd0),
dtifit.ad,
decimal=5)
def draw_ellipsoid(data, gtab, outliers, data_without_noise):
bvecs = gtab.bvecs
raw_data = bvecs * np.array([data, data, data]).T
ren = fvtk.ren()
# Draw Sphere of predicted data
new_sphere = sphere.Sphere(xyz=bvecs[~gtab.b0s_mask, :])
sf1 = fvtk.sphere_funcs(data_without_noise[~gtab.b0s_mask],
new_sphere,
colormap=None,
norm=False,
scale=1)
sf1.GetProperty().SetOpacity(0.35)
fvtk.add(ren, sf1)
# Draw Raw Data as points, Red means outliers
good_values = [index for index, voxel in enumerate(outliers) if voxel == 0]
bad_values = [index for index, voxel in enumerate(outliers) if voxel == 1]
point_actor_good = fvtk.point(raw_data[good_values, :],
fvtk.colors.yellow,
point_radius=.05)
point_actor_bad = fvtk.point(raw_data[bad_values, :],
fvtk.colors.red,
point_radius=0.05)
fvtk.add(ren, point_actor_good)
fvtk.add(ren, point_actor_bad)
fvtk.show(ren)
def draw_p(p, x):
ren = fvtk.ren()
raw_data = x * np.array([p, p, p])
#point = fvtk.point(raw_data, fvtk.colors.red, point_radius=0.00001)
#fvtk.add(ren, point)
#fvtk.show(ren)
new_sphere = sphere.Sphere(xyz=x)
sf1 = fvtk.sphere_funcs(raw_data,
new_sphere,
colormap=None,
norm=False,
scale=0)
sf1.GetProperty().SetOpacity(0.35)
fvtk.add(ren, sf1)
def odf_peaks(self):
"""
Calculate the value of each of the peaks in the ODF using the dipy
peak-finding algorithm
"""
faces = sphere.Sphere(xyz=self.bvecs[:, self.b_idx].T).faces
odf_flat = self.odf[self.mask]
out_flat = np.zeros(odf_flat.shape)
for vox in xrange(odf_flat.shape[0]):
if np.all(np.isfinite(odf_flat[vox])):
peaks, inds = recspeed.local_maxima(odf_flat[vox], faces)
out_flat[vox][inds] = peaks
out = np.zeros(self.odf.shape)
out[self.mask] = out_flat
return out
def draw_points(data, gtab, predicted_data):
ren = fvtk.ren()
bvecs = gtab.bvecs
raw_points = bvecs * np.array([data, data, data]).T
predicted_points = bvecs * np.array(
[predicted_data, predicted_data, predicted_data]).T
# Draw Raw Data as points, Red means outliers
point = fvtk.point(raw_points[~gtab.b0s_mask, :],
fvtk.colors.red,
point_radius=0.02)
fvtk.add(ren, point)
new_sphere = sphere.Sphere(xyz=bvecs[~gtab.b0s_mask, :])
sf1 = fvtk.sphere_funcs(predicted_data[~gtab.b0s_mask],
new_sphere,
colormap=None,
norm=False,
scale=0)
sf1.GetProperty().SetOpacity(0.35)
fvtk.add(ren, sf1)
fvtk.show(ren)
def draw_adc(D_noisy, D, threeD=False):
# 3D Plot
if threeD == True:
amound = 50
alpha = np.linspace(0, 2 * np.pi, amound)
theta = np.linspace(0, 2 * np.pi, amound)
vector = np.empty((amound**2, 3))
vector[:, 0] = (np.outer(np.sin(theta), np.cos(alpha))).reshape(
(-1, 1))[:, 0]
vector[:, 1] = (np.outer(np.sin(theta), np.sin(alpha))).reshape(
(-1, 1))[:, 0]
vector[:, 2] = (np.outer(np.cos(theta), np.ones(amound))).reshape(
(-1, 1))[:, 0]
adc_noisy = np.empty((vector.shape[0], 3))
shape_noisy = np.empty((vector.shape[0], 1))
shape = np.empty((vector.shape[0], 1))
for i in range(vector.shape[0]):
adc_noisy[i] = np.dot(
vector[i], np.dot(vector[i], np.dot(D_noisy, vector[i].T)))
shape_noisy[i] = np.dot(vector[i], np.dot(D_noisy, vector[i].T))
shape[i] = np.dot(vector[i], np.dot(D, vector[i].T))
ren = fvtk.ren()
# noisy sphere
new_sphere = sphere.Sphere(xyz=vector)
sf1 = fvtk.sphere_funcs(shape_noisy[:, 0],
new_sphere,
colormap=None,
norm=False,
scale=0.0001)
sf1.GetProperty().SetOpacity(0.35)
sf1.GetProperty().SetColor((1, 0, 0))
fvtk.add(ren, sf1)
# ideal sphere
sf2 = fvtk.sphere_funcs(shape[:, 0],
new_sphere,
colormap=None,
norm=False,
scale=0.0001)
sf2.GetProperty().SetOpacity(0.35)
fvtk.add(ren, sf2)
#point_actor_bad = fvtk.point(adc_noisy, fvtk.colors.red, point_radius=0.00003)
#fvtk.add(ren, point_actor_bad)
fvtk.show(ren)
# 2D Plot in XY-Plane
alpha = np.linspace(0, 2 * np.pi, 100)
vector = np.empty((100, 3))
vector[:, 0] = np.cos(alpha)
vector[:, 1] = np.sin(alpha)
vector[:, 2] = 0.0
adc_noisy_2d = np.empty((vector.shape[0], 3))
adc_2d = np.empty((vector.shape[0], 3))
for i in range(vector.shape[0]):
adc_noisy_2d[i] = np.dot(
vector[i], np.dot(vector[i], np.dot(D_noisy, vector[i].T)))
adc_2d[i] = np.dot(vector[i], np.dot(vector[i], np.dot(D,
vector[i].T)))
# Change Axis so that there is 20% room on each side of the plot
x = np.concatenate((adc_noisy_2d, adc_2d), axis=0)
minimum = np.min(x, axis=0)
maximum = np.max(x, axis=0)
plt.plot(adc_noisy_2d[:, 0], adc_noisy_2d[:, 1], 'r')
plt.plot(adc_2d[:, 0], adc_2d[:, 1], 'b')
plt.axis([
minimum[0] * 1.2, maximum[0] * 1.2, minimum[1] * 1.2, maximum[1] * 1.2
])
plt.xlabel('ADC (mm2*s-1)')
plt.ylabel('ADC (mm2*s-1)')
red_patch = mpatches.Patch(color='red', label='Noisy ADC')
blue_patch = mpatches.Patch(color='blue', label='Ideal ADC')
plt.legend(handles=[red_patch, blue_patch])
plt.show()
snr = 20
fodf_power = 25
M = 5
'''sphere_sig = get_sphere('repulsion200')
v_sig = sphere_sig.vertices
n_sig = v_sig.shape[0]
#sphere_fod = get_sphere('symmetric362')
sphere_fod = get_sphere('repulsion724')
v_fod = sphere_fod.vertices
n_fod = v_fod.shape[0]'''
n_sig = 200
Xp, Yp, Zp = crl_aux.distribute_on_sphere_spiral(n_sig)
sphere_sig = dipysphere.Sphere(Xp, Yp, Zp)
v_sig, _ = sphere_sig.vertices, sphere_sig.faces
n_fod = 724
Xp, Yp, Zp = crl_aux.distribute_on_sphere_spiral(n_fod)
sphere_fod = dipysphere.Sphere(Xp, Yp, Zp)
v_fod, _ = sphere_fod.vertices, sphere_fod.faces
###########
n_fib = 1
CSF_range = [0.0, 0.40]
n_fib_sim = 3000000
X_train_1 = np.zeros((n_fib_sim, n_sig), np.float32)
def get_mitk_sphere():
""" Return MITK compliant dipy Sphere object.
MITK stores ODFs as 252 values spherically sampled from the continuous ODF.
The sampling directions are generate by a 5-fold subdivisions of an icosahedron.
"""
xyz = np.array([
0.9756767549555488, 0.9977154378498742, 0.9738192119472443,
0.8915721200771204, 0.7646073555341725, 0.6231965669156312,
0.9817040172417226, 0.9870396762453547, 0.9325589150767597,
0.8173592116492303, 0.6708930871960926, 0.9399233672993689,
0.9144882783890762, 0.8267930935417315, 0.6931818659696647,
0.8407280774774689, 0.782394344826989, 0.6762337155773353,
0.7005607434301688, 0.6228579759074076, 0.5505632701289798,
0.4375940376503738, 0.3153040621970065, 0.1569517536476641,
-0.01984099037382634, -0.1857690950088067, -0.3200730131503601,
0.5232435944036425, 0.3889403678268736, 0.2135250052622625,
0.02420694871807206, -0.1448539951504302, 0.5971534158422009,
0.4482053228282282, 0.2597018771197477, 0.06677517278138323,
0.6404616222418184, 0.4782876117785159, 0.2868761951248767,
0.6459894362878276, 0.4789651252338281, 0.3200724178002418,
0.4973180497018747, 0.6793811951363423, 0.8323587928990375,
0.9308933612987835, 0.4036036036586492, 0.5984781165037405,
0.7817280923310203, 0.9140795130247613, 0.4809905907165384,
0.6759621154318279, 0.8390728924802671, 0.5347729120192694,
0.7094340284155564, 0.5560356639783846, 0.2502538949373057,
0.3171352000240629, 0.3793963897789465, 0.4231100429674418,
0.4410301813437042, 0.4357529867703999, 0.5208717223808415,
0.5850086433327374, 0.611055499882272, 0.6009463532173235,
0.6305067000562991, 0.7188806066405239, 0.7654898954879897,
0.7616477696596397, 0.7997756996573342, 0.8700831379830764,
0.8872031228985237, 0.9155019734809123, 0.9568003701205341,
-0.4375932291383153, -0.3153035222278598, -0.1569515927579475,
0.0198407706589918, 0.1857686171195431, -0.2644927501381796,
-0.1064219080255857, 0.07849995612144045, 0.2583107784678281,
-0.04938676750055992, 0.1358448755096817, 0.3243479900672576,
0.1811879481039926, 0.3692668145365748, 0.3890115016151001,
-0.6231952788307174, -0.4943551945928708, -0.319458133528771,
-0.1156489798772063, 0.08328895892415776, -0.4789641985801549,
-0.3127252940830145, -0.1059392282183739, 0.1077444781964869,
0.2912280153186658, -0.2868758523956744, -0.08856892011805101,
0.1287405357080231, 0.3245517154572714, -0.06677541204276306,
0.1413542883070481, 0.3408430926744944, 0.1448534358763926,
0.3374016489097037, -0.2502532651867688, -0.3171345072414974,
-0.3793956104585266, -0.4231091882680272, -0.4410293135613324,
-0.09929959410007272, -0.1535127609134815, -0.2052877394623771,
-0.2436963810571767, 0.08175409117371149, 0.04056025153798869,
-0.006048944565669369, 0.2686152102237028, 0.2319923070602857,
0.430309819720559, -0.975676581463901, -0.9977153903038788,
-0.9738191090293654, -0.8915716840571059, -0.7646064477130079,
-0.9568001079664734, -0.9598482725023617, -0.9044523389503778,
-0.7901672201648241, -0.6459882395962464, -0.8872027729137049,
-0.8582754834679532, -0.7705800268610806, -0.6404605781008121,
-0.7616472974254324, -0.7008201753656432, -0.5971525097007422,
-0.6009457148226922, -0.5232427588825813, 0.4943566966479628,
0.3194596781650836, 0.1156503154178581, -0.0832879858164388,
0.5222841738261358, 0.3225497922064885, 0.1018140973507329,
0.5217885230992481, 0.3044789836562512, 0.4873191346491355,
-0.4973183240635209, -0.6793811856410323, -0.8323586364840968,
-0.9308931819742911, -0.3374020539278631, -0.5261951664998159,
-0.7070125356849136, -0.8417962075837926, -0.9155017573317124,
-0.3408433114184408, -0.5265312606271311, -0.6896418460594331,
-0.7997755164970677, -0.3245517106425898, -0.4925847482169691,
-0.6305065080228541, -0.2912277152063287, -0.4357526334612896,
0.7901679726328494, 0.9044526665335126, 0.9598484396937114,
0.7705806468939737, 0.858275831469383, 0.7008207681995118,
-0.4036039458806759, -0.2583110138480089, -0.0784999126587471,
0.1064223584250461, 0.264493571710179, -0.4809907334514471,
-0.3243480295764106, -0.1358446002697818, 0.04938746901646566,
-0.5347730026038946, -0.3692667658371347, -0.1811875286592425,
-0.5560358190148772, -0.3890114324926668, -0.5505634949474449,
0.8417963565884857, 0.7070125813068046, 0.5261950179989611,
0.6896418985458221, 0.5265311900255359, 0.4925848265160583,
0.2436972866599269, 0.2052886581368649, 0.153513629451971,
0.09930039009433847, 0.006049691633511915, -0.04055950638179381,
-0.08175337578691833, -0.2319919155781195, -0.2686148310916902,
-0.430309819678344, -0.02420720081803753, -0.2135248270679241,
-0.3889397838050994, -0.2597016312374675, -0.4482046405142344,
-0.4782867918076852, -0.1018130528605821, -0.322548598821141,
-0.5222830294256716, -0.6708921376896406, -0.304478224282928,
-0.5217878437313506, -0.6931813485878851, -0.4873188675145023,
-0.6762335873429084, -0.6228580878699612, -0.6110548409057,
-0.5850080622199078, -0.5208712693637837, -0.7654894328832393,
-0.7188802647693375, -0.8700828159137221, -0.8173587433845655,
-0.9325588839421305, -0.9870397834787261, -0.9817039872478999,
-0.8267930492778305, -0.9144884914916022, -0.9399235077793813,
-0.7823945479956939, -0.8407283372889187, -0.7005610213599369,
-0.1077438933887955, 0.1059400956623477, 0.3127262866621893,
-0.1287403742204129, 0.08856921814263634, -0.1413545191115968,
-0.9140794058749131, -0.7817279594934516, -0.5984781448346268,
-0.8390728949381593, -0.6759620794963979, -0.709434131000089,
-0.1778161375899129, -0.06053925384414331, 0.07929679392711581,
0.222673458561735, 0.3458247516791153, 0.4366423972091846,
0.01030826616734189, 0.1591522280204451, 0.3173816763430465,
0.4549463955350546, 0.5521270265729551, 0.2292788658415479,
0.3973400932411465, 0.5502139834879405, 0.6594089221868847,
0.4476465561008348, 0.6096570464011057, 0.7343998566036512,
0.629214796874201, 0.7646693979379596, 0.7580253719184178,
-0.5980610514568761, -0.5101530988159087, -0.382225667160838,
-0.2244621267538426, -0.06301328229424107, 0.07805400320688782,
-0.4311039309963852, -0.3079662136138592, -0.1501157132113724,
0.01750888497279251, 0.1650825345160538, -0.2148810450151756,
-0.06090095222676627, 0.1073128739652992, 0.2584097661066967,
0.02655484252908358, 0.1901297170957776, 0.3420822257932489,
0.2531835106264871, 0.4022303494272352, -0.07805410188638827,
-0.1080255529483224, -0.1376217050758367, -0.1609000070073124,
-0.1740018618448228, 0.09827676798573926, 0.083291898217249,
0.06127443921955168, 0.03526739273256396, 0.2991139104294396,
0.2941068360088736, 0.2692865316145088, 0.4942032775296958,
0.4857723178878524, 0.6512069539966677, -0.9161616153729886,
-0.9396953110011561, -0.9204280785344878, -0.8462030522374957,
-0.7293237120999879, -0.8470541513588044, -0.8482966176587544,
-0.7977006542517769, -0.6951661565374421, -0.566558592627622,
-0.7243096319272092, -0.6931460376496088, -0.6140043047773551,
-0.5016343691560573, -0.5520254073275178, -0.4928644880867128,
-0.403575153350467, -0.3587591578566765, -0.2886351685087218,
0.5980613131647216, 0.5101532951859686, 0.382225843595672,
0.2244622808787926, 0.06301334452030186, 0.6944632949786616,
0.5955168212825119, 0.4473425940100297, 0.2700417838303327,
0.7724043956082883, 0.6553545192922715, 0.4871408620353512,
0.8097301284690857, 0.6725220182496192, 0.8002534097038426,
-0.4366431953633789, -0.5869882376922511, -0.7332080507197046,
-0.8450980113065225, -0.9041113586460733, -0.4022310083998925,
-0.554596445154436, -0.6925605687496104, -0.7854318984598006,
-0.8250621271173465, -0.3420827953668352, -0.4840440064641756,
-0.6033456975789954, -0.6777531805937266, -0.2584102557043402,
-0.3819753792546441, -0.4821906665520286, -0.1650828712784331,
-0.270790845781693, 0.9161619708326184, 0.9396956424389374,
0.9204283182965946, 0.8462032095340455, 0.7293238793541417,
0.9749588444840027, 0.9879501207294071, 0.942053498973333,
0.8348196077814718, 0.9950795014807369, 0.9818515654328379,
0.9027098746674149, 0.9581801446138297, 0.9118246030313639,
0.8703772282258925, 0.1778171059718252, 0.06053992567271226,
-0.07929659020903117, -0.2226737578340799, -0.345825401239635,
0.2886360377097776, 0.1672516508448342, 0.02000533874392893,
-0.1285435155191929, -0.2531843553864728, 0.403575906447316,
0.2774342678683828, 0.1245598363284875, -0.02655554762561945,
0.5016349858535857, 0.3695530582277636, 0.2148806720954671,
0.5665590425344393, 0.431103930292903, 0.5869876102086139,
0.7332077514676827, 0.845098078457225, 0.9041116580482536,
0.7182616282077119, 0.8617334421407644, 0.9490975365686583,
0.8223898048944452, 0.9416915744235097, 0.8729720010540123,
0.1080256414522809, 0.1376220280275969, 0.1609005865750696,
0.1740026689030255, 0.2707904196202965, 0.3196768235430837,
0.3552546724685221, 0.3677018240803483, 0.3587598208776521,
0.4821901792282771, 0.5389508449256169, 0.5637713635689835,
0.5520258363563475, 0.6777529577987501, 0.7231337276202411,
0.724309982145211, 0.8250622687013296, 0.8470545173149734,
0.1285429999155006, -0.02000532948058562, -0.1672511147059996,
-0.1245600244829796, -0.2774338902981233, -0.3695528631494325,
-0.09827641615811868, -0.2700412859530667, -0.4473420975374328,
-0.5955164071695848, -0.6944629164413806, -0.2991130971968019,
-0.4871400501186961, -0.6553538941234454, -0.7724039524031648,
-0.4942022299541438, -0.6725212074710563, -0.8097296395344389,
-0.6512059956089504, -0.8002528392148971, -0.7580246814516085,
-0.3677014077761052, -0.3552545716101517, -0.3196770257819652,
-0.5637712030900536, -0.5389510214534028, -0.7231336172569296,
-0.8348194119106425, -0.9420533966954356, -0.9879499956150448,
-0.9749586635216289, -0.9027097279159257, -0.9818515951566739,
-0.9950795477220543, -0.9118244750171576, -0.9581802235578871,
-0.8703770126934449, -0.0175091339170676, 0.1501155140512474,
0.3079660822386824, -0.1073133727582037, 0.06090046334304851,
-0.1901304002696938, -0.9490974969653682, -0.8617336589899791,
-0.7182621005240754, -0.5521276321758419, -0.941691783045487,
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0.403998910419839, 0.62060207699311, 0.7967976318501995,
0.4726965405256068, 0.6757048258462731, 0.5106167801856609])
n = int(xyz.shape[0] / 3)
x = xyz[:n]
y = xyz[n:2 * n]
z = xyz[2 * n:]
for i in range(n):
v = np.array([x[i], y[i], z[i]])
norm = np.linalg.norm(v)
if norm > 0:
v /= norm
x[i] = v[0]
y[i] = v[1]
z[i] = v[2]
s = sphere.Sphere(x=x, y=y, z=z)
return s
