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@@ -3,60 +3,62 @@
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#from scipy.special import gamma, binom
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import numpy as np
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import matplotlib.pyplot as plt
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-
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+from scipy.special import gamma
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from mpmath import mp, mpf
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-mp.dps = 200
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+mp.dps = 1000
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voxel_num = 5
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-phase_range = mp.pi/2
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-phase_init = mp.pi/4 #mpf(0.0)
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+phase_range = np.pi/2
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+phase_init = np.pi/4 #(0.0)
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U_points = voxel_num * 1000
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-noise_ratio = mpf(0.0*1e-38) #1e8
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+noise_ratio = (0.0*1e-38) #1e8
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total_periods = 10
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-rf_samples_per_period = 10
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+rf_samples_per_period = 20
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# max polynomial order equals rf_samples_per_period * total_periods
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# B0=1.5T freq=64Mhz, period = 15.6 ns
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-period = mpf(10) #ms
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-omega = 2.0*mp.pi/period
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+period = (10) #ms
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+omega = 2.0*np.pi/period
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#T2s_scale = 0.01 #ms # need to be 10ms
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T2s_scale = total_periods*period/15 #ms # need to be 10ms
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T2s_min = T2s_scale/10.0
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#print(period)
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#ms
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-time_steps = np.array(mp.linspace(mpf(0), mpf(period*total_periods), rf_samples_per_period*total_periods))
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-tmp = [mp.rand() for n in range(voxel_num)]
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+time_steps = np.array(np.linspace((0), (period*total_periods), rf_samples_per_period*total_periods))
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+tmp = [np.random.rand() for n in range(voxel_num)]
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voxel_amplitudes = np.array(tmp)
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-tmp = [mp.rand() for n in range(voxel_num)]
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+tmp = [np.random.rand() for n in range(voxel_num)]
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voxel_T2s_decay = np.array(tmp)*(T2s_scale-2*T2s_min) + T2s_min
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print(voxel_T2s_decay)
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voxel_all = np.append(voxel_amplitudes,voxel_T2s_decay/T2s_scale)
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if voxel_num == 5:
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-# voxel_all = np.array([mpf(0.2),mpf(0.6),mpf(0.02),mpf(0.1)])
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- voxel_all = np.array([mpf(0.822628),mpf(0.691376),mpf(0.282906),mpf(0.226013),mpf(0.90703),mpf(0.144985),mpf(0.228563),mpf(0.340353),mpf(0.462462),mpf(0.720518)])
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- #voxel_all = np.array([mpf(0.592606),mpf(0.135168),mpf(0.365712),mpf(0.667536),mpf(0.437378),mpf(0.918822),mpf(0.943879),mpf(0.590338),mpf(0.685997),mpf(0.658839)])
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+# voxel_all = np.array([(0.2),(0.6),(0.02),(0.1)])
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+ voxel_all = np.array([(0.822628),(0.691376),(0.282906),(0.226013),(0.90703),(0.144985),(0.228563),(0.340353),(0.462462),(0.720518)])
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+ #voxel_all = np.array([(0.592606),(0.135168),(0.365712),(0.667536),(0.437378),(0.918822),(0.943879),(0.590338),(0.685997),(0.658839)])
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voxel_amplitudes = voxel_all[:voxel_num]
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voxel_T2s_decay = voxel_all[voxel_num:]*T2s_scale
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-# a_i = np.array([mpf(0.3),mpf(0.1),mpf(0.15),mpf(0.1)])
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-# d_i = np.array([mpf(0.7),mpf(0.9),mpf(0.2),mpf(0.67)])
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+# a_i = np.array([(0.3),(0.1),(0.15),(0.1)])
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+# d_i = np.array([(0.7),(0.9),(0.2),(0.67)])
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# voxel_num = len(a_i)
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-voxel_phases = np.array(mp.linspace(0,phase_range, voxel_num))
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+voxel_phases = np.array(np.linspace(0,phase_range, voxel_num))
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# if len(voxel_amplitudes) != len(voxel_phases):
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# print("ERROR! Size of amplitude and phase arrays do not match!")
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# raise
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ampl = []
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+
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+
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def gen_rf_signal(time):
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'''Generates demodulated signal at radio frequence using voxels
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amplitudes, T2s decays, and phases.
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'''
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- tmp = [mpf(0.0) for n in range(len(time))]
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+ tmp = [(0.0) for n in range(len(time))]
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mag_sin = np.array(tmp)
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mag_cos = np.array(tmp)
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for t in range(len(time)):
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@@ -69,17 +71,17 @@ def gen_rf_signal(time):
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# "d_{:d} =".format(i),float(d_i[i]),"+", np.random.rand()*noise_ratio)
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amp = voxel_amplitudes[i] * (
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- mp.exp(-time[t]/voxel_T2s_decay[i])
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+ np.exp(-time[t]/voxel_T2s_decay[i])
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) + ( 0.0
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# + np.random.rand()*noise_ratio
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)
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if t == 0:
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- #print("a_{:d}".format(i),float(voxel_amplitudes[i]* mp.sin(voxel_phases[i] + phase_init)))
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- print("d_{:d}".format(i),float( mp.exp(-(period/rf_samples_per_period)/voxel_T2s_decay[i]) ))
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- mag_sin[t] += amp * mp.sin(
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+ #print("a_{:d}".format(i),float(voxel_amplitudes[i]* np.sin(voxel_phases[i] + phase_init)))
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+ print("d_{:d}".format(i),float( np.exp(-(period/rf_samples_per_period)/voxel_T2s_decay[i]) ))
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+ mag_sin[t] += amp * np.sin(
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voxel_phases[i] + phase_init
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)
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- mag_cos[t] += amp * mp.cos(
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+ mag_cos[t] += amp * np.cos(
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voxel_phases[i] + phase_init
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)
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return mag_sin, mag_cos
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@@ -93,7 +95,7 @@ def binom(n,k):
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def shiftedLegendre(n):
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coeffs = []
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for k in range(n+1):
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- val = mpf(-1)**n * binom(mpf(n),mpf(k)) * binom(n+k,k) * (-1)**k
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+ val = mpf(-1)**mpf(n) * binom(mpf(n),mpf(k)) * binom(mpf(n+k),mpf(k)) * mpf(-1)**mpf(k)
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coeffs.insert(0,val*mp.sqrt(2*n+1))
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return np.poly1d(coeffs)
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@@ -102,15 +104,18 @@ def K ( i, j):
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return polyL.coeffs[-j-1]
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def GetU (lambdas):
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- x = np.array(mp.linspace(0,1, U_points))
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- tmp = [mpf(0.0) for n in range(len(lambdas))]
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- mag_sin = np.array(tmp)
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- tmp = [mpf(0.0) for n in range(U_points)]
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- U = np.array(tmp)
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+ n_max = len(lambdas)
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+ P = np.ones((n_max+1,U_points))
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+ x = np.linspace(0,1, U_points)
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+ P[1] = 2*x-1
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+ for n in range(1,n_max):
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+ P[n+1] = ((2*n+1)/(n+1)) * (2*x-1) * P[n] - (n/(n+1))*P[n-1]
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+
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+ U = np.zeros(U_points)
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for i in range (len(lambdas)):
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if i%10 == 0: print(i)
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- polyL = L[i] #shiftedLegendre(i)
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- U += lambdas[i]*polyL(x)
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+ #polyL = L[i] #shiftedLegendre(i)
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+ U += lambdas[i]*P[i]*np.sqrt(2*i+1)
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return U
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def GetLambda(mag_rf):
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@@ -118,14 +123,14 @@ def GetLambda(mag_rf):
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all_lambda = []
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for i in range(M_cutoff):
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# print("M = ", i)
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- lambd = mpf(0)
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+ lambd = (0)
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for j in range(i+1):
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- lambd += K(i,j)*mag_rf[j]
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+ lambd += float(K(i,j))*mag_rf[j]
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# print("K({:d},{:d}) =".format(i,j), float(K(i,j)))
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all_lambda.append(lambd)
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- # tmp = [mpf(0.0) for n in range(M_cutoff)]
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+ # tmp = [(0.0) for n in range(M_cutoff)]
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# all_lambda = np.array(tmp)
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- # all_lambda[10] = mpf(1.0)
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+ # all_lambda[10] = (1.0)
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return all_lambda
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@@ -137,7 +142,7 @@ sign = ""
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# sign+="\n"
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# sign = sign + '{:3.2g}'.format(float(a_i[i]))+"/"+'{:3.2g}'.format(float(d_i[i]))+", "
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-# # print(mp.exp(-1.0/voxel_T2s_decay[i]))
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+# # print(np.exp(-1.0/voxel_T2s_decay[i]))
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plt.plot(mag_sin, ls=' ', marker='o')
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@@ -157,14 +162,14 @@ x = np.linspace(0,1, U_points)
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polyL_val = np.array([float(L[-1](x[n])) for n in range(U_points)])
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-plt.plot(x,polyL_val)
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-plt.title("Legendre polynom of order "+str(len(L)))
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-plt.savefig("polyL.pdf")
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-plt.clf()
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-print("Output of last poly done.")
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+# plt.plot(x,polyL_val)
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+# plt.title("Legendre polynom of order "+str(len(L)))
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+# plt.savefig("polyL.pdf")
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+# plt.clf()
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+# print("Output of last poly done.")
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lambdas = GetLambda(mag_sin)
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-print(len(lambdas))
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+print(lambdas)
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U = GetU(lambdas)
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x = np.linspace(0,1, U_points)
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