def test_t2stimulate_b1refocussing(response):
# Basic Curve
curve = stimulate(alpha=np.pi, num_echoes=5, T1=1000, T2=80, tau=5)
assert np.allclose(curve, np.array([0.8824969, 0.77880078, 0.68728928, 0.60653066, 0.53526143]))
# Lower flip angle Curve
curve = stimulate(alpha=170 / 180 * np.pi, num_echoes=5, T1=1000, T2=80, tau=5)
assert np.allclose(curve, np.array([0.87579335, 0.78018687, 0.68201487, 0.60873607, 0.53107402]))
# 90 degree refocussing flip angle
curve = stimulate(alpha=90 / 180 * np.pi, num_echoes=5, T1=1000, T2=80, tau=5)
assert np.allclose(curve, np.array([0.44124845, 0.63155815, 0.55734811, 0.47468203, 0.41890542]))
def test_t2stimulate_t2(response):
curve = stimulate(alpha=160 / 180 * np.pi, num_echoes=10, T1=500, T2=40, tau=5)
assert np.allclose(curve, np.array([0.75531707, 0.61515309, 0.45765074, 0.37871017, 0.27706674,
0.23331114, 0.16756293, 0.14391496, 0.10110945, 0.08902654]))
curve = stimulate(alpha=160 / 180 * np.pi, num_echoes=10, T1=500, T2=150, tau=5)
assert np.allclose(curve, np.array([0.907298, 0.87682296, 0.79552223, 0.7674361, 0.69872935,
0.67070899, 0.61443793, 0.58571367, 0.54050228, 0.51152747]))
curve = stimulate(alpha=160 / 180 * np.pi, num_echoes=10, T1=1000, T2=40, tau=5)
assert np.allclose(curve, np.array([0.75531707, 0.61560182, 0.45749782, 0.37940138, 0.27675072,
0.23410671, 0.16712624, 0.14472953, 0.10059922, 0.08981685]))
curve = stimulate(alpha=160 / 180 * np.pi, num_echoes=10, T1=1000, T2=150, tau=5)
assert np.allclose(curve, np.array([0.907298, 0.87736198, 0.79550237, 0.76837305, 0.6987273,
0.67188742, 0.61451089, 0.5869956, 0.54069289, 0.51281792]))
def test_t2stimulate_t2(response):
curve = stimulate(alpha=160 / 180 * np.pi, num_echoes=10, T1=1000, T2=20, tau=5)
assert np.allclose(curve, np.array([0.58824152, 0.38115041, 0.21238988, 0.14887362, 0.0732573,
0.06113308, 0.02222788, 0.02757839, 0.0038753, 0.01443641]))
curve = stimulate(alpha=160 / 180 * np.pi, num_echoes=10, T1=1000, T2=80, tau=5)
assert np.allclose(curve, np.array([0.85588636, 0.78364414, 0.6675115, 0.6131273, 0.52151495,
0.47890863, 0.40811059, 0.37358872, 0.31967318, 0.29129722]))
curve = stimulate(alpha=160 / 180 * np.pi, num_echoes=10, T1=1000, T2=140, tau=5)
assert np.allclose(curve, np.array([0.9029878, 0.86930194, 0.78420596, 0.75432046, 0.68227728,
0.65352062, 0.59438338, 0.5656607, 0.51807706, 0.48957907]))
curve = stimulate(alpha=160 / 180 * np.pi, num_echoes=10, T1=1000, T2=300, tau=5)
assert np.allclose(curve, np.array([0.93805097, 0.93594812, 0.87919668, 0.8744822, 0.8253415,
0.81601533, 0.77550069, 0.76105825, 0.72872955, 0.71001549]))
def test_t2stimulate_numechoes(response):
result = np.array([0.85588636, 0.78364414, 0.6675115, 0.6131273, 0.52151495,
0.47890863, 0.40811059, 0.37358872, 0.31967318, 0.29129722,
0.25038159, 0.22727689, 0.19587117, 0.17762711, 0.15289925,
0.13915641, 0.11904341, 0.10929365, 0.09245346, 0.08601781,
0.07166901, 0.06778384, 0.05550148, 0.05343787, 0.04296809,
0.04212754, 0.03325952, 0.03321771, 0.02572334, 0.02622135,
0.01984966, 0.02075043])
# Few echoes
curve = stimulate(alpha=160 / 180 * np.pi, num_echoes=3, T1=1000, T2=80, tau=5)
assert np.allclose(curve, result[:3])
# Few echoes
curve = stimulate(alpha=160 / 180 * np.pi, num_echoes=15, T1=1000, T2=80, tau=5)
assert np.allclose(curve, result[:15])
# 32 echoes
curve = stimulate(alpha=160 / 180 * np.pi, num_echoes=32, T1=1000, T2=80, tau=5)
assert np.allclose(curve, result)
def test_t2stimulate_b1refocussing(response):
# Basic Curve
num_echoes = 64
tau = 5
component_initial = Component(985, 22, 900)
curve = component_initial.pd * stimulate(alpha=160 / 180 * np.pi,
num_echoes=num_echoes,
T1=component_initial.t1,
T2=component_initial.t2,
tau=tau)
curve_e = np.sqrt((curve + np.random.normal(0, 5, num_echoes))**2 +
np.random.normal(0, 5, num_echoes)**2)
te = 2 * tau * np.arange(1, num_echoes + 1)
components_initial = (component_initial, )
b1_initial = 170 / 180 * np.pi
x = fit(te, curve_e, components_initial, b1_initial)
print(x, 160 / 180 * np.pi)
assert True
import matplotlib.pyplot as plt
import numpy as np
from t2stimulate.simulate import stimulate
# Set a couple of parameters
num_echoes = 32
tau = 5
T1 = 1000
te = 2 * tau * np.arange(1, num_echoes + 1)
curve_140 = stimulate(alpha=140 / 180 * np.pi,
num_echoes=num_echoes,
T1=450,
T2=80,
tau=tau)
curve_160 = stimulate(alpha=160 / 180 * np.pi,
num_echoes=num_echoes,
T1=450,
T2=80,
tau=tau)
curve_180 = stimulate(alpha=180 / 180 * np.pi,
num_echoes=num_echoes,
T1=450,
T2=80,
tau=tau)
plt.figure(1)
plt.clf()