In [1]:

    

from __future__ import division
# %pylab
import numpy as np
from numpy import pi
import epg


    

In [2]:

    

np.set_printoptions(suppress=True, precision=4, linewidth=300)


    

In [3]:

    

T1 = 1000e-3
T2 = 100e-3
TE = 5e-3

P_z = np.array([[0],[0],[1]])
P_xy = np.array([[1],[1],[0]])

def eRF(P, a, p):
    return epg.rf(P, np.pi/180 * a, np.pi/180*p)

def eTE(P, a, p):
    return epg.FSE_TE(P, a * np.pi/180, p * np.pi / 180, TE, T1, T2)

def eRelax(P, T):
    return epg.relax(P, T, T1, T2)

def eGrad(P):
    return epg.grad(P)


    

In [4]:

    

import matplotlib.pyplot as plt
%matplotlib inline


    

In [5]:

    

P1 = epg.rf(P_z, 160*pi/180, pi/2)
print P1
P2 = epg.relax(P1, 15e-3, T1, T2)
P3 = epg.rf(P2, pi/2, pi/2)
print P3
P4 = epg.FSE_TE(P3, 180*pi/180, 0, TE, T1, T2)
print P4


    

    




[[ 0.3420-0.j]
 [ 0.3420+0.j]
 [-0.9397+0.j]]
[[-0.9108+0.j]
 [-0.9108-0.j]
 [-0.2944+0.j]]
[[-0.8664-0.j  0.0000+0.j -0.0000+0.j]
 [-0.8664+0.j  0.0000+0.j  0.0000+0.j]
 [ 0.2929+0.j  0.0000+0.j  0.0000+0.j]]

In [6]:

    

flip_rad = np.array([0 + 90j, 90 + 90j, 120, 120, 120, 120, 120, 120, 120]) * pi / 180
TI = 15e-3
Mxy, Mz = epg.FSE_signal2(np.real(flip_rad), np.imag(flip_rad), TE, T1, T2, TI)
print Mxy[:3]
fig = plt.figure()
plt.plot(np.real(Mxy))
plt.plot(np.imag(Mxy))
plt.plot(Mz)
plt.plot(np.abs(Mxy), 'k')
plt.legend(('real mxy', 'imag mxy', 'mz', 'signal'))


    

    




[[ 1.0000-0.j]
 [ 0.7134+0.j]
 [ 0.8639-0.j]]






    
Out[6]:





<matplotlib.legend.Legend at 0x110ca30d0>






    

In [11]:

    

flip_rad = np.array([0 + 90j, 90 + 90j, 120, 100, 90, 80, 70, 60, 30, 30, 30, 90, 0, 0, 0, 0, 0, 0, 0]) * pi / 180
TI = 0e-3
T1=1000e-3
T1=500e-3
Mxy, Mz = epg.FSE_signal2(np.real(flip_rad), np.imag(flip_rad), TE, T1, T2, TI)
print Mxy[:3]
fig = plt.figure()
plt.plot(np.real(Mxy))
plt.plot(np.imag(Mxy))
plt.plot(Mz)
plt.plot(np.abs(Mxy), 'k')
plt.legend(('real mxy', 'imag mxy', 'mz', 'signal'))


    

    




[[ 1.0000-0.j]
 [ 0.7134+0.j]
 [ 0.7998+0.j]]






    
Out[11]:





<matplotlib.legend.Legend at 0x11123b310>






    

In [8]:

    

T1 = 600e-3;
P = epg.rf(P_z, pi, pi/2)
P = epg.relax(P, 10e-3, T1, T2)

flip_rad = np.array([90, 180, 180, 180, 180, 10, 0, 80, 100]) * pi / 180
Mxy, Mz = epg.FSE_signal2(np.real(flip_rad), np.imag(flip_rad), TE, T1, T2, P)

fig = plt.figure()
plt.plot(np.abs(Mxy))
plt.plot(Mz)


    

    




---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-8-bdc1865c24d3> in <module>()
      4 
      5 flip_rad = np.array([90, 180, 180, 180, 180, 10, 0, 80, 100]) * pi / 180
----> 6 Mxy, Mz = epg.FSE_signal2(np.real(flip_rad), np.imag(flip_rad), TE, T1, T2, P)
      7 
      8 fig = plt.figure()

/Volumes/Drive/Drive/school/research/cs-mri/svn/jtamir/projects/mri-sim-py/epg/epg.pyc in FSE_signal2(angles_rad, phase_rad, TE, T1, T2, TI, M0)
    270 
    271     P = np.copy(M0)
--> 272     if TI > 0.:
    273         P = rf(P, angles_rad[0], phase_rad[0]) # apply inversion pulse
    274         P = relax(P, TI, T1, T2) # wait inversion time

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

In [ ]:

    

eTE(eRF(eTE(eRF(P_z, 180, 90),0, 0), 90, 90), 180, 0)


    

In [ ]:

    

np.hstack((np.array([0,1]), np.array([2,3])))


    

In [ ]:

    

epg.rf(np.array([0,0,1]).T,pi/3, 0)


    

In [92]:

    

np.insert?


    

In [ ]: