mirror of
https://github.com/wassname/simpeg.git
synced 2026-06-29 17:21:30 +08:00
Jvec working for MT1D, Jtvec getting close
This commit is contained in:
GudniRos
@@ -159,7 +159,7 @@
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"source": [
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"As matrices the formulas above can be written as\n",
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"\\begin{align}\n",
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"\\left[ \\frac{\\partial P(f(m))}{\\partial u} v \\right] = diag \\left[ \\frac{1}{\\left(P_b \\frac{1}{mu_0} f_b(u)\\right)} \\right] [P_e v] - diag[P_e u] diag \\left[ \\frac{1}{\\left(P_b \\frac{1}{mu_0} f_b(u)\\right)} \\right]^T diag \\left[ \\frac{1}{\\left(P_b \\frac{1}{mu_0} f_b(u)\\right)} \\right] \\left[ P_b \\frac{d f_b}{du}(v) \\frac{1}{mu_0} \\right]\n",
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"\\left[ \\frac{\\partial P(f(m))}{\\partial u} v \\right] = \\left[ diag \\left[ \\frac{1}{\\left(P_b \\frac{1}{mu_0} f_b(u)\\right)} \\right] [P_e v] , diag[P_e u] diag \\left[ \\frac{1}{\\left(P_b \\frac{1}{mu_0} f_b(u)\\right)} \\right]^T diag \\left[ \\frac{1}{\\left(P_b \\frac{1}{mu_0} f_b(u)\\right)} \\right] \\left[ P_b \\frac{1}{mu_0} \\frac{d f_b}{du}(v) \\right] \\right]\n",
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"\\end{align}\n",
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"\n"
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]
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@@ -170,7 +170,7 @@
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"source": [
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"The adjoint problem is done simliarly\n",
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"\\begin{align}\n",
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"\\left[ \\frac{\\partial P(f(m))}{\\partial u} v \\right]^T = [P_e v]^T diag \\left[ \\frac{1}{\\left(P_b \\frac{1}{mu_0} f_b(u)\\right)} \\right]^T - \\left[ P_b \\frac{d f_b}{du}(v) \\frac{1}{mu_0} \\right]^T diag \\left[ \\frac{1}{\\left(P_b \\frac{1}{mu_0} f_b(u)\\right)} \\right] diag \\left[ \\frac{1}{\\left(P_b \\frac{1}{mu_0} f_b(u)\\right)} \\right]^T diag \\left[ P_e u \\right]^T\n",
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"\\left[ \\frac{\\partial P(f(m))}{\\partial u} v \\right]^T = [P_e]^T diag \\left[ \\frac{1}{\\left(P_b \\frac{1}{mu_0} f_b(u)\\right)} \\right]^T v - \\left[ P_b \\frac{d f_b}{du} \\frac{1}{mu_0} \\right]^T diag \\left[ \\frac{1}{\\left(P_b \\frac{1}{mu_0} f_b(u)\\right)} \\right] diag \\left[ \\frac{1}{\\left(P_b \\frac{1}{mu_0} f_b(u)\\right)} \\right]^T diag \\left[ P_e u \\right]^T v\n",
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"\\end{align}\n"
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]
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},
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@@ -210,135 +210,21 @@
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"src = survey.srcList[0]\n",
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"rx = src.rxList[0]\n",
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"v = np.random.randn(m1d.nN)\n",
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"v0 = np.random.randn(m1d.nF+m1d.nE)\n",
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"u0 = np.random.randn(m1d.nN)+np.random.randn(m1d.nN)*1j\n",
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"f0 = problem.fieldsPair(m1d,survey)\n",
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"f0[src,'e_1dSolution'] = u0\n",
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"# f0[src,'b_1d'] = -1/(1j*simpegem.Utils.EMUtils.omega(src.freq))*m1d.nodalGrad*u0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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||||
"array([ -5.21576299e-01-0.28596337j, 1.33775588e+00-1.08159078j,\n",
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" -2.34257640e+00+1.16351971j, -5.41859146e-01+1.0902067j ,\n",
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" 2.47173181e-04-0.41345406j, 1.00840211e+00+1.23027562j,\n",
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" 8.39952932e-01+0.43605496j, 8.65132399e-01+1.07984908j,\n",
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" 2.04841885e+00-0.0851883j , 1.44345869e-01+0.4539564j ,\n",
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" 7.08238379e-01+0.536495j , 5.90147090e-01+0.60041602j,\n",
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" 7.22415122e-01+1.59030908j, -5.13304114e-01-0.09690537j,\n",
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" 5.41180474e-01+2.6467645j , -8.05664508e-01+1.44276035j,\n",
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" 4.29526056e-02+0.16497284j, 5.92169353e-01-0.76120082j,\n",
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" 3.43892536e-01+0.3488206j , -2.42917431e+00-0.25699335j,\n",
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" 5.53836789e-01-0.45887772j, -6.75365309e-01+0.0848306j ,\n",
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|
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" -8.95365798e-01+1.22241712j, -4.65516271e-01-0.10833261j,\n",
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" -1.55272666e-01-1.20074828j, 5.78006258e-01+0.06705178j,\n",
|
||||
" 9.35873450e-01-1.244475j , 3.08977268e-03-1.57055076j,\n",
|
||||
" -1.69903140e+00-1.00183406j, -5.73059946e-01-1.96431137j,\n",
|
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" 3.80255478e-01-0.58071467j, -3.27026898e-01-0.44312791j,\n",
|
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" 2.66653209e-01+0.20442809j, -7.26239447e-02-0.73470193j,\n",
|
||||
" -1.27190710e+00+0.6681173j , -1.66240293e+00-0.68067832j,\n",
|
||||
" -1.30894453e-01+0.4142016j , 1.17940864e-01-0.29430427j,\n",
|
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" 5.40779365e-01-0.1570116j , 4.59411844e-01-1.33449408j,\n",
|
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|
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" 3.23000023e-01+0.87737735j, 1.05597723e-02-0.74774027j,\n",
|
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" -2.25509504e+00+2.81870339j, -9.95775953e-01-1.21962899j,\n",
|
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" -8.59196834e-01+1.4407182j , -6.38861601e-01+0.67564119j,\n",
|
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" 1.99028382e-01+2.32353483j, -1.15758435e+00-0.70250997j,\n",
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" -9.18642125e-01-0.72232602j, 1.00415977e+00+0.40547797j,\n",
|
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" -2.61807248e-01-1.27673902j, -1.39773535e+00+1.04765733j,\n",
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" 8.93091651e-01-0.67650682j, -1.26058855e+00+1.24753141j,\n",
|
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" -4.41649966e-01-0.64575466j, -1.35393159e+00+0.42326246j,\n",
|
||||
" 1.35067221e-01+1.44186272j, 3.95784662e-01-0.99344302j,\n",
|
||||
" 3.22900419e-01+1.13931166j])"
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||||
]
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||||
},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"u0"
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]
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},
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{
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"#rx.projectFieldsDeriv(src,m1d,fields,v)\n"
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]
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 7,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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@@ -351,12 +237,13 @@
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"==================== checkDerivative ====================\n",
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"iter h |ft-f0| |ft-f0-h*J0*dx| Order\n",
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"---------------------------------------------------------\n",
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" 0 1.00e-01 5.703e-05 1.929e-06 nan\n",
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" 1 1.00e-02 5.877e-06 1.954e-08 1.994\n",
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" 2 1.00e-03 5.894e-07 1.956e-10 1.999\n",
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" 3 1.00e-04 5.896e-08 1.957e-12 2.000\n",
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" 0 1.00e-01 3.893e-05 5.627e-08 nan\n",
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" 1 1.00e-02 3.898e-06 5.632e-10 2.000\n",
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" 2 1.00e-03 3.898e-07 5.633e-12 2.000\n",
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" 3 1.00e-04 3.898e-08 5.633e-14 2.000\n",
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" 4 1.00e-05 3.898e-09 5.637e-16 2.000\n",
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"========================= PASS! =========================\n",
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"Not just a pretty face Gudni\n",
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"Yay passed!\n",
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"\n"
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]
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},
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@@ -366,7 +253,7 @@
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"True"
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]
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},
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"execution_count": 8,
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -376,9 +263,30 @@
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"def fun(u):\n",
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" f = problem.fieldsPair(m1d,survey)\n",
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" f[src,'e_1dSolution'] = u\n",
|
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"# f[src,'b_1d'] = -(m1d.nodalGrad*u)/(1j*simpegem.Utils.EMUtils.omega(src.freq))\n",
|
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" return rx.projectFields(src,m1d,f), lambda t: rx.projectFieldsDeriv(src,m1d,f0,t)\n",
|
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"simpeg.Tests.checkDerivative(fun,u0,num=4,plotIt=False)"
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"simpeg.Tests.checkDerivative(fun,u0,num=5,plotIt=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
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"array(-0.005683351098616839)"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"rx.projectFieldsDeriv(src,m1d,f0,u0)"
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]
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},
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{
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@@ -387,6 +295,28 @@
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"metadata": {
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||||
"collapsed": false
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},
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||||
"outputs": [
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{
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||||
"data": {
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||||
"text/plain": [
|
||||
"array([[ 0.00374478]])"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"rx.projectFields(src,m1d,f0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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||||
"metadata": {
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||||
"collapsed": false
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||||
},
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"outputs": [],
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"source": [
|
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"# Test the Jvec derivative."
|
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@@ -394,7 +324,7 @@
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},
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{
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"cell_type": "code",
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||||
"execution_count": 10,
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||||
"execution_count": 11,
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"metadata": {
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||||
"collapsed": false
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||||
},
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@@ -407,25 +337,26 @@
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"iter h |ft-f0| |ft-f0-h*J0*dx| Order\n",
|
||||
"---------------------------------------------------------\n",
|
||||
"Project at freq: 1.000e+02\n",
|
||||
"Project at freq: 1.000e+02\n"
|
||||
"Project at freq: 1.000e+02\n",
|
||||
" 0 1.00e-01 7.283e-06 9.326e-09 nan\n",
|
||||
"Project at freq: 1.000e+02\n",
|
||||
" 1 1.00e-02 7.290e-07 9.338e-11 1.999\n",
|
||||
"Project at freq: 1.000e+02\n",
|
||||
" 2 1.00e-03 7.290e-08 9.339e-13 2.000\n",
|
||||
"========================= PASS! =========================\n",
|
||||
"Testing is important.\n",
|
||||
"\n"
|
||||
]
|
||||
},
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{
|
||||
"ename": "ValueError",
|
||||
"evalue": "dimension mismatch",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-10-44b23b96d204>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0msurvey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpred\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mproblem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mJvec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0msimpeg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheckDerivative\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mu0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplotIt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpeg/SimPEG/Tests/TestUtils.pyc\u001b[0m in \u001b[0;36mcheckDerivative\u001b[1;34m(fctn, x0, num, plotIt, dx, expectedOrder, tolerance, eps, ax)\u001b[0m\n\u001b[0;32m 256\u001b[0m \u001b[1;31m# 1st order Taylor\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 257\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0minspect\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mJ0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 258\u001b[1;33m \u001b[0mE1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ml2norm\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mft\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mf0\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mJ0\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 259\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 260\u001b[0m \u001b[1;31m# We assume it is a numpy.ndarray\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m<ipython-input-10-44b23b96d204>\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mfun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0msurvey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpred\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mproblem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mJvec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 15\u001b[0m \u001b[0msimpeg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheckDerivative\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mu0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplotIt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpegmt/simpegMT/BaseMT.pyc\u001b[0m in \u001b[0;36mJvec\u001b[1;34m(self, m, v, f)\u001b[0m\n\u001b[0;32m 64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 66\u001b[1;33m \u001b[0mJv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrx\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mP\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdu_dm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 67\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmkvc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mJv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpegmt/simpegMT/BaseMT.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(v)\u001b[0m\n\u001b[0;32m 61\u001b[0m \u001b[0mdu_dm\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mdf_dm\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m \u001b[0mP\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mrx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprojectFieldsDeriv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmesh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# wrt u, also have wrt m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpegmt/simpegMT/SurveyMT.py\u001b[0m in \u001b[0;36mprojectFieldsDeriv\u001b[1;34m(self, src, mesh, f, v, adjoint)\u001b[0m\n\u001b[0;32m 141\u001b[0m \u001b[1;31m# ex = Pex*mkvc(f[src,'e_1d'],2)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 142\u001b[0m \u001b[1;31m# bx = Pbx*mkvc(f[src,'b_1d'],2)/mu_0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 143\u001b[1;33m \u001b[0mderiv_complex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msdiag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPbx\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmkvc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'b_1d'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mmu_0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPex\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msdiag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPex\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmkvc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'e_1d'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msdiag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPbx\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmkvc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'b_1d'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mmu_0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msdiag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPbx\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmkvc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'b_1d'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mmu_0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPbx\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_bDeriv_u\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mmu_0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 144\u001b[0m \u001b[1;31m# elif self.projType is 'Z2D\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 145\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprojType\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;34m'Z3D'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m/home/gudni/anaconda/lib/python2.7/site-packages/scipy/sparse/base.pyc\u001b[0m in \u001b[0;36m__mul__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m 325\u001b[0m \u001b[1;31m# dense row or column vector\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 326\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 327\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'dimension mismatch'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 328\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 329\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mul_vector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mValueError\u001b[0m: dimension mismatch"
|
||||
]
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
@@ -442,24 +373,64 @@
|
||||
"# survey = prb.survey\n",
|
||||
"def fun(x):\n",
|
||||
" \n",
|
||||
" return survey.dpred(m0), lambda x: problem.Jvec(m0, x)\n",
|
||||
"simpeg.Tests.checkDerivative(fun, u0, num=3, plotIt=False)"
|
||||
" return survey.dpred(x), lambda x: problem.Jvec(m0, x)\n",
|
||||
"simpeg.Tests.checkDerivative(fun, m0, num=3, plotIt=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"### Adjoint test"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We have \n",
|
||||
"\\begin{align}\n",
|
||||
"Jvec =&"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"problem.getADeriv_m(freq,fields[src,'e_1dSolution'],v)"
|
||||
"TOL = 1e-4\n",
|
||||
"FLR = 1e-20\n",
|
||||
"\n",
|
||||
"def adjointTest(fdemType, comp):\n",
|
||||
" print 'Adjoint %s formulation - %s' % (fdemType, comp)\n",
|
||||
"\n",
|
||||
" m = np.log(np.ones(problem.mesh.nC)*0.01)\n",
|
||||
" if True:\n",
|
||||
" m = m + np.random.randn(problem.mesh.nC)*0.01*1e-1 \n",
|
||||
"\n",
|
||||
" u = problem.fields(m)\n",
|
||||
"\n",
|
||||
" v = np.random.rand(survey.nD)\n",
|
||||
" # print prb.PropMap.PropModel.nP\n",
|
||||
" w = np.random.rand(problem.mesh.nC)\n",
|
||||
"\n",
|
||||
" vJw = v.dot(problem.Jvec(m, w, u))\n",
|
||||
" wJtv = w.dot(problem.Jtvec(m, v, u))\n",
|
||||
" tol = np.max([TOL*(10**int(np.log10(np.abs(vJw)))),FLR]) \n",
|
||||
" print vJw, wJtv, vJw - wJtv, tol, np.abs(vJw - wJtv) < tol\n",
|
||||
" return np.abs(vJw - wJtv) < tol"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -468,21 +439,53 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"> \u001b[1;32m/home/gudni/anaconda/lib/python2.7/site-packages/scipy/sparse/base.py\u001b[0m(327)\u001b[0;36m__mul__\u001b[1;34m()\u001b[0m\n",
|
||||
"\u001b[1;32m 326 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0m\u001b[1;32m--> 327 \u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'dimension mismatch'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0m\u001b[1;32m 328 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"Adjoint E formulation - e\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "IndexError",
|
||||
"evalue": "tuple index out of range",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-14-250b6ff497c4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0madjointTest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'E'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'e'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[1;32m<ipython-input-13-3fac9635b176>\u001b[0m in \u001b[0;36madjointTest\u001b[1;34m(fdemType, comp)\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[0mvJw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproblem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mJvec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m \u001b[0mwJtv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproblem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mJtvec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 19\u001b[0m \u001b[0mtol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mTOL\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog10\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvJw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mFLR\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mvJw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwJtv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvJw\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mwJtv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtol\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvJw\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mwJtv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mtol\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpegmt/simpegMT/BaseMT.pyc\u001b[0m in \u001b[0;36mJtvec\u001b[1;34m(self, m, v, f)\u001b[0m\n\u001b[0;32m 102\u001b[0m \u001b[0mPTv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprojectFieldsDeriv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmesh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0madjoint\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# wrt u, need possibility wrt m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 103\u001b[0m \u001b[1;31m# Get the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 104\u001b[1;33m \u001b[0mdA_duIT\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mATinv\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mPTv\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 105\u001b[0m \u001b[0mdA_dmT\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetADeriv_m\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mu_src\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdA_duIT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0madjoint\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 106\u001b[0m \u001b[0mdRHS_dmT\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetRHSDeriv_m\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdA_duIT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0madjoint\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpeg/SimPEG/Utils/SolverUtils.pyc\u001b[0m in \u001b[0;36m__mul__\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m 35\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Can only multiply by a numpy array.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 36\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 37\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 38\u001b[0m \u001b[0mb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 39\u001b[0m \u001b[1;31m# Just one RHS\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mIndexError\u001b[0m: tuple index out of range"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"adjointTest('E','e')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"> \u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpeg/SimPEG/Utils/SolverUtils.py\u001b[0m(37)\u001b[0;36m__mul__\u001b[1;34m()\u001b[0m\n",
|
||||
"\u001b[1;32m 36 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0m\u001b[1;32m---> 37 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0m\u001b[1;32m 38 \u001b[1;33m \u001b[0mb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0m\n",
|
||||
"ipdb> u\n",
|
||||
"> \u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpegmt/simpegMT/SurveyMT.py\u001b[0m(143)\u001b[0;36mprojectFieldsDeriv\u001b[1;34m()\u001b[0m\n",
|
||||
"\u001b[1;32m 142 \u001b[1;33m \u001b[1;31m# bx = Pbx*mkvc(f[src,'b_1d'],2)/mu_0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0m\u001b[1;32m--> 143 \u001b[1;33m \u001b[0mderiv_complex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msdiag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPbx\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmkvc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'b_1d'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mmu_0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPex\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msdiag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPex\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmkvc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'e_1d'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msdiag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPbx\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmkvc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'b_1d'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mmu_0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msdiag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPbx\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmkvc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'b_1d'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mmu_0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPbx\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_bDeriv_u\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mmu_0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0m\u001b[1;32m 144 \u001b[1;33m \u001b[1;31m# elif self.projType is 'Z2D\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"> \u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpegmt/simpegMT/BaseMT.py\u001b[0m(104)\u001b[0;36mJtvec\u001b[1;34m()\u001b[0m\n",
|
||||
"\u001b[1;32m 103 \u001b[1;33m \u001b[1;31m# Get the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0m\u001b[1;32m--> 104 \u001b[1;33m \u001b[0mdA_duIT\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mATinv\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mPTv\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0m\u001b[1;32m 105 \u001b[1;33m \u001b[0mdA_dmT\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetADeriv_m\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mu_src\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdA_duIT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0madjoint\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0m\n",
|
||||
"ipdb> Pex*v\n",
|
||||
"*** ValueError: dimension mismatch\n",
|
||||
"ipdb> v.shape\n",
|
||||
"(180,)\n"
|
||||
"ipdb> PTv\n",
|
||||
"array(-6.9926641874700175e-06)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -498,7 +501,8 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"problem.getA"
|
||||
"v = np.random.randn(1,1) + 1j* np.random.randn(1,1)\n",
|
||||
"rx.projectFieldsDeriv(src, problem.mesh, fields, v, adjoint=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
+38
-39
@@ -2,7 +2,8 @@ from simpegEM.FDEM import BaseFDEMProblem
|
||||
from SurveyMT import SurveyMT
|
||||
from DataMT import DataMT
|
||||
from FieldsMT import FieldsMT
|
||||
from SimPEG import SolverLU as SimpegSolver
|
||||
from SimPEG import SolverLU as SimpegSolver, mkvc
|
||||
import numpy as np
|
||||
|
||||
class BaseMTProblem(BaseFDEMProblem):
|
||||
|
||||
@@ -24,18 +25,32 @@ class BaseMTProblem(BaseFDEMProblem):
|
||||
# Might need to add more stuff here.
|
||||
|
||||
def Jvec(self, m, v, f=None):
|
||||
"""
|
||||
Function to calculate the data sensitivities dD/dm times a vector.
|
||||
|
||||
:param numpy.ndarray (nC, 1) - conductive model
|
||||
:param numpy.ndarray (nC, 1) - random vector
|
||||
:param MTfields object (optional) - MT fields object, if not given it is calculated
|
||||
:rtype: MTdata object
|
||||
:return: Data sensitivities wrt m
|
||||
"""
|
||||
|
||||
# Calculate the fields
|
||||
if f is None:
|
||||
f = self.fields(m)
|
||||
|
||||
# Set current model
|
||||
self.curModel = m
|
||||
|
||||
# Initiate the Jv object
|
||||
Jv = self.dataPair(self.survey)
|
||||
|
||||
# Loop all the frequenies
|
||||
for freq in self.survey.freqs:
|
||||
dA_du = self.getA(freq) #
|
||||
dA_duI = self.Solver(dA_du, **self.solverOpts)
|
||||
|
||||
for src in self.survey.getSrcByFreq(freq):
|
||||
# We need fDeriv_m = df/du*du/dm + df/dm
|
||||
# Construct du/dm, it requires a solve
|
||||
ftype = self._fieldType + 'Solution'
|
||||
u_src = f[src, ftype]
|
||||
dA_dm = self.getADeriv_m(freq, u_src, v)
|
||||
@@ -44,28 +59,23 @@ class BaseMTProblem(BaseFDEMProblem):
|
||||
du_dm = dA_duI * ( - dA_dm )
|
||||
else:
|
||||
du_dm = dA_duI * ( - dA_dm + dRHS_dm )
|
||||
# Calculate the projection derivatives
|
||||
for rx in src.rxList:
|
||||
# df_duFun = u.deriv_u(rx.fieldsUsed, m)
|
||||
if 'e' in self._fieldType:
|
||||
projField = 'b'
|
||||
elif 'b' in self._fieldType:
|
||||
projField = 'e'
|
||||
df_duFun = getattr(f, '_%sDeriv_u'%projField, None)
|
||||
df_du = df_duFun(src, du_dm, adjoint=False)
|
||||
if df_du is not None:
|
||||
du_dm = df_du
|
||||
# Get the stacked derivative
|
||||
# df_duFun = getattr(f, '_fDeriv_u', None)
|
||||
# df_dmFun = getattr(f, '_fDeriv_m', None)
|
||||
# df_dm = df_dmFun(src,v,adjoint=False)
|
||||
# if df_dm is None:
|
||||
# fDeriv_m = df_duFun(src, du_dm, adjoint=False)
|
||||
# else:
|
||||
# fDeriv_m = df_duFun(src, du_dm, adjoint=False) + df_dm
|
||||
# Not needed for now. Since PDeriv does this currently.
|
||||
|
||||
df_dmFun = getattr(f, '_%sDeriv_m'%projField, None)
|
||||
df_dm = df_dmFun(src, v, adjoint=False)
|
||||
if df_dm is not None:
|
||||
du_dm += df_dm
|
||||
|
||||
P = lambda v: rx.projectFieldsDeriv(src, self.mesh, f, v) # wrt u, also have wrt m
|
||||
|
||||
|
||||
Jv[src, rx] = P(du_dm)
|
||||
|
||||
return Utils.mkvc(Jv)
|
||||
# Get the projection derivative
|
||||
PDeriv = lambda v: rx.projectFieldsDeriv(src, self.mesh, f, v) # wrt u, also have wrt m
|
||||
Jv[src, rx] = PDeriv(du_dm)
|
||||
# Return the vectorized sensitivities
|
||||
return mkvc(Jv)
|
||||
|
||||
def Jtvec(self, m, v, f=None):
|
||||
if f is None:
|
||||
@@ -88,29 +98,18 @@ class BaseMTProblem(BaseFDEMProblem):
|
||||
u_src = f[src, ftype]
|
||||
|
||||
for rx in src.rxList:
|
||||
# Get the adjoint projectFieldsDeriv
|
||||
PTv = rx.projectFieldsDeriv(src, self.mesh, f, v[src, rx], adjoint=True) # wrt u, need possibility wrt m
|
||||
|
||||
df_duTFun = getattr(f, '_%sDeriv_u'%rx.projField, None)
|
||||
df_duT = df_duTFun(src, PTv, adjoint=True)
|
||||
if df_duT is not None:
|
||||
dA_duIT = ATinv * df_duT
|
||||
else:
|
||||
dA_duIT = ATinv * PTv
|
||||
|
||||
# Get the
|
||||
dA_duIT = ATinv * PTv
|
||||
dA_dmT = self.getADeriv_m(freq, u_src, dA_duIT, adjoint=True)
|
||||
|
||||
dRHS_dmT = self.getRHSDeriv_m(src, dA_duIT, adjoint=True)
|
||||
|
||||
# Make du_dmT
|
||||
if dRHS_dmT is None:
|
||||
du_dmT = - dA_dmT
|
||||
else:
|
||||
du_dmT = -dA_dmT + dRHS_dmT
|
||||
|
||||
df_dmFun = getattr(f, '_%sDeriv_m'%rx.projField, None)
|
||||
dfT_dm = df_dmFun(src, PTv, adjoint=True)
|
||||
if dfT_dm is not None:
|
||||
du_dmT += dfT_dm
|
||||
|
||||
# Select the correct component
|
||||
real_or_imag = rx.projComp
|
||||
if real_or_imag == 'real':
|
||||
Jtv += du_dmT.real
|
||||
|
||||
+29
-4
@@ -80,10 +80,11 @@ class FieldsMT_1D(FieldsMT):
|
||||
return - 1./(1j*omega(src.freq)) * (C * v)
|
||||
|
||||
def _bSecondaryDeriv_m(self, src, v, adjoint = False):
|
||||
S_mDeriv, _ = src.evalDeriv(self.survey.prob, adjoint)
|
||||
S_mDeriv = S_mDeriv(v)
|
||||
if S_mDeriv is not None:
|
||||
return 1./(1j * omega(src.freq)) * S_mDeriv
|
||||
# Doesn't depend on m
|
||||
# _, S_eDeriv = src.evalDeriv(self.survey.prob, adjoint)
|
||||
# S_eDeriv = S_eDeriv(v)
|
||||
# if S_eDeriv is not None:
|
||||
# return 1./(1j * omega(src.freq)) * S_eDeriv
|
||||
return None
|
||||
|
||||
def _bDeriv_u(self, src, v, adjoint=False):
|
||||
@@ -94,3 +95,27 @@ class FieldsMT_1D(FieldsMT):
|
||||
# Assuming the primary does not depend on the model
|
||||
return self._bSecondaryDeriv_m(src, v, adjoint)
|
||||
|
||||
def _fDeriv_u(self, src, v, adjoint=False):
|
||||
"""
|
||||
Derivative of the fields object wrt u.
|
||||
|
||||
:param MTsrc src: MT source
|
||||
:param numpy.ndarray v: random vector of f_sol.size
|
||||
This function stacks the fields derivatives appropriately
|
||||
|
||||
return a vector of size (nreEle+nrbEle)
|
||||
"""
|
||||
|
||||
de_du = v #Utils.spdiag(np.ones((self.nF,)))
|
||||
db_du = self._bDeriv_u(src, v, adjoint)
|
||||
# Return the stack
|
||||
# This doesn't work...
|
||||
return np.vstack((de_du,db_du))
|
||||
|
||||
def _fDeriv_m(self, src, v, adjoint=False):
|
||||
"""
|
||||
Derivative of the fields object wrt m.
|
||||
|
||||
This function stacks the fields derivatives appropriately
|
||||
"""
|
||||
return None
|
||||
@@ -60,7 +60,8 @@ class eForm_psField(BaseMTProblem):
|
||||
if adjoint:
|
||||
return 1j * omega(freq) * ( dMf_dsig.T * v )
|
||||
# Note: output has to be nN/nF, not nC/nE.
|
||||
return 1j * omega(freq) * ( (dMf_dsig * dMf_dsig.T)**(1/2) * v)
|
||||
# v should be nC
|
||||
return 1j * omega(freq) * ( dMf_dsig * v )
|
||||
|
||||
def getRHS(self, freq):
|
||||
"""
|
||||
@@ -106,17 +107,11 @@ class eForm_psField(BaseMTProblem):
|
||||
|
||||
# Store the fields
|
||||
Src = self.survey.getSrcByFreq(freq)[0]
|
||||
# Calculate total e
|
||||
# NOTE: only store the e_solution(secondary), all other components calculated in the fields object
|
||||
F[Src, 'e_1dSolution'] = e_s[:,1] # Only storing the yx polarization as 1d
|
||||
|
||||
e = Src.ePrimary(self) + e_s
|
||||
|
||||
# Store the fields
|
||||
# NOTE: only store
|
||||
F[Src, 'e_1dSolution'] = e[:,1] # Only storing the yx polarization as 1d
|
||||
# F[Src, 'e_py'] = 0*e[:,0]
|
||||
# Note curl e = -iwb so b = -curl e /iw
|
||||
# b = -( self.mesh.nodalGrad * e )/( 1j*omega(freq) )
|
||||
# F[Src, 'b_px'] = 0*b[:,0]
|
||||
# F[Src, 'b_1d'] = b[:,1]
|
||||
if self.verbose:
|
||||
print 'Ran for {:f} seconds'.format(time.time()-startTime)
|
||||
|
||||
+28
-11
@@ -85,7 +85,7 @@ class RxMT(Survey.BaseRx):
|
||||
|
||||
def projectFields(self, src, mesh, f):
|
||||
'''
|
||||
Project the fields and return the
|
||||
Project the fields and return the correct data.
|
||||
'''
|
||||
|
||||
if self.projType is 'Z1D':
|
||||
@@ -131,32 +131,48 @@ class RxMT(Survey.BaseRx):
|
||||
def projectFieldsDeriv(self, src, mesh, f, v, adjoint=False):
|
||||
"""
|
||||
The derivative of the projection wrt u
|
||||
|
||||
:param MTsrc src: MT source
|
||||
:param TensorMesh mesh: Mesh defining the topology of the problem
|
||||
:param MTfields f: MT fields object of the source
|
||||
:param numpy.ndarray v: Random vector of size
|
||||
"""
|
||||
|
||||
real_or_imag = self.projComp
|
||||
|
||||
if not adjoint:
|
||||
if self.projType is 'Z1D':
|
||||
Pex = mesh.getInterpolationMat(self.locs,'Fx')
|
||||
Pbx = mesh.getInterpolationMat(self.locs,'Ex')
|
||||
# ex = Pex*mkvc(f[src,'e_1d'],2)
|
||||
# bx = Pbx*mkvc(f[src,'b_1d'],2)/mu_0
|
||||
deriv_complex = Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0))*(Pex*v) - Utils.sdiag(Pex*mkvc(f[src,'e_1d'],2))*(Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0)).T*Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0)))*(Pbx*f._bDeriv_u(src,v)/mu_0)
|
||||
dP_de = Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0))*(Pex*v)
|
||||
dP_db = - Utils.sdiag(Pex*mkvc(f[src,'e_1d'],2))*(Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0)).T*Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0)))*(Pbx*f._bDeriv_u(src,v)/mu_0)
|
||||
PDeriv_complex = np.sum((dP_de,dP_db))
|
||||
# elif self.projType is 'Z2D
|
||||
elif self.projType is 'Z3D':
|
||||
pass
|
||||
Pv = getattr(deriv_complex, real_or_imag)
|
||||
raise NotImplementedError('Has not be implement for full impedance tensor')
|
||||
Pv = np.array(getattr(PDeriv_complex, real_or_imag))
|
||||
elif adjoint:
|
||||
raise NotImplementedError('must be real or imag')
|
||||
|
||||
if self.projType is 'Z1D':
|
||||
Pex = mesh.getInterpolationMat(self.locs,'Fx')
|
||||
Pbx = mesh.getInterpolationMat(self.locs,'Ex')
|
||||
# ex = Pex*mkvc(f[src,'e_1d'],2)
|
||||
# bx = Pbx*mkvc(f[src,'b_1d'],2)/mu_0
|
||||
dP_deTv = mkvc(Pex.T*Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0)).T*v,2)
|
||||
db_duv = Pbx.T/mu_0*Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0))*(Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0))).T*Utils.sdiag(Pex*mkvc(f[src,'e_1d'],2)).T
|
||||
dP_dbTv = -mkvc(f._bDeriv_u(src,db_duv,adjoint=True)*v,2)
|
||||
PDeriv_complex = np.sum((dP_deTv,dP_dbTv))
|
||||
elif self.projType is 'Z3D':
|
||||
raise NotImplementedError('must be real or imag')
|
||||
Pv = np.array(getattr(PDeriv_complex, real_or_imag))
|
||||
return Pv
|
||||
|
||||
|
||||
# Note: Might need to add tests to make sure that both polarization have the same rxList.
|
||||
|
||||
###############
|
||||
### Sources ###
|
||||
###############
|
||||
# Note: Should like inheret from FDEM
|
||||
|
||||
class srcMT(SrcFDEM): # Survey.BaseSrc):
|
||||
'''
|
||||
Sources for the MT problem.
|
||||
@@ -238,15 +254,16 @@ class srcMT_polxy_1Dprimary(srcMT):
|
||||
if problem.mesh.dim == 1:
|
||||
# Need to use the faceInnerProduct
|
||||
MsigmaDeriv = problem.mesh.getFaceInnerProductDeriv(problem.curModel.sigma)(self.ePrimary(problem)[:,-1]) * problem.curModel.sigmaDeriv
|
||||
MsigmaDeriv = ( MsigmaDeriv * MsigmaDeriv.T)**2
|
||||
# MsigmaDeriv = ( MsigmaDeriv * MsigmaDeriv.T)**2
|
||||
if problem.mesh.dim == 2:
|
||||
pass
|
||||
if problem.mesh.dim == 3:
|
||||
MsigmaDeriv = problem.MeSigmaDeriv(self.ePrimary(problem))
|
||||
if adjoint:
|
||||
#
|
||||
return MsigmaDeriv.T * v
|
||||
else:
|
||||
# Moved the v in front to make the multi work
|
||||
# v should be nC size
|
||||
return MsigmaDeriv * v
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user