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Working Jvec for the MT problem - not currently working. Dimensional mismatch with matrices.

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This commit is contained in:
GudniRos
2015-06-23 08:31:44 -07:00
parent 60b6c24e19
commit 2cbfe2d6b9
5 changed files with 500 additions and 195 deletions
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notebooks/Derivative test MT1D.ipynb
+281 -156
View File
@@ -13,7 +13,7 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
"collapsed": false
},
"outputs": [],
"source": [
@@ -29,6 +29,28 @@
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"simpegMT.FieldsMT.FieldsMT_1D"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"simpegmt.FieldsMT.FieldsMT_1D"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
@@ -85,6 +107,15 @@
"data = survey.projectFields(fields)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
@@ -113,7 +144,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 4,
"metadata": {
"collapsed": true
},
@@ -128,7 +159,7 @@
"source": [
"As matrices the formulas above can be written as\n",
"\\begin{align}\n",
"\\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 \\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",
"\\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",
"\\end{align}\n",
"\n"
]
@@ -139,13 +170,13 @@
"source": [
"The adjoint problem is done simliarly\n",
"\\begin{align}\n",
"\\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 \n",
"\\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",
"\\end{align}\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 5,
"metadata": {
"collapsed": true
},
@@ -169,7 +200,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 6,
"metadata": {
"collapsed": false
},
@@ -178,15 +209,16 @@
"# Initate things for the derivs Test\n",
"src = survey.srcList[0]\n",
"rx = src.rxList[0]\n",
"v = np.random.randn(m1d.nN)\n",
"u0 = np.random.randn(m1d.nN)+np.random.randn(m1d.nN)*1j\n",
"f0 = problem.fieldsPair(m1d,survey)\n",
"f0[src,'e_1d'] = u0\n",
"f0[src,'b_1d'] = -1/(1j*simpegem.Utils.EMUtils.omega(src.freq))*m1d.nodalGrad*u0"
"f0[src,'e_1dSolution'] = u0\n",
"# f0[src,'b_1d'] = -1/(1j*simpegem.Utils.EMUtils.omega(src.freq))*m1d.nodalGrad*u0"
]
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": true
@@ -195,100 +227,100 @@
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" -1.61059835e-01-0.62180822j, -1.75918449e-01-0.31350392j,\n",
" -2.28918438e-01-1.25884104j, -3.94756005e-01-0.08464047j,\n",
" 3.23000023e-01+0.87737735j, 1.05597723e-02-0.74774027j,\n",
" -2.25509504e+00+2.81870339j, -9.95775953e-01-1.21962899j,\n",
" -8.59196834e-01+1.4407182j , -6.38861601e-01+0.67564119j,\n",
" 1.99028382e-01+2.32353483j, -1.15758435e+00-0.70250997j,\n",
" -9.18642125e-01-0.72232602j, 1.00415977e+00+0.40547797j,\n",
" -2.61807248e-01-1.27673902j, -1.39773535e+00+1.04765733j,\n",
" 8.93091651e-01-0.67650682j, -1.26058855e+00+1.24753141j,\n",
" -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])"
]
},
"execution_count": 6,
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -306,7 +338,63 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==================== checkDerivative ====================\n",
"iter h |ft-f0| |ft-f0-h*J0*dx| Order\n",
"---------------------------------------------------------\n",
" 0 1.00e-01 5.703e-05 1.929e-06 nan\n",
" 1 1.00e-02 5.877e-06 1.954e-08 1.994\n",
" 2 1.00e-03 5.894e-07 1.956e-10 1.999\n",
" 3 1.00e-04 5.896e-08 1.957e-12 2.000\n",
"========================= PASS! =========================\n",
"Not just a pretty face Gudni\n",
"\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Run a test\n",
"def fun(u):\n",
" f = problem.fieldsPair(m1d,survey)\n",
" f[src,'e_1dSolution'] = u\n",
"# f[src,'b_1d'] = -(m1d.nodalGrad*u)/(1j*simpegem.Utils.EMUtils.omega(src.freq))\n",
" return rx.projectFields(src,m1d,f), lambda t: rx.projectFieldsDeriv(src,m1d,f0,t)\n",
"simpeg.Tests.checkDerivative(fun,u0,num=4,plotIt=False)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Test the Jvec derivative."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
@@ -318,39 +406,60 @@
"==================== checkDerivative ====================\n",
"iter h |ft-f0| |ft-f0-h*J0*dx| Order\n",
"---------------------------------------------------------\n",
" 0 1.00e-01 1.969e-05 8.331e-07 nan\n",
" 1 1.00e-02 2.045e-06 7.979e-09 2.019\n",
" 2 1.00e-03 2.052e-07 7.945e-11 2.002\n",
" 3 1.00e-04 2.052e-08 7.942e-13 2.000\n",
"========================= PASS! =========================\n",
"That was easy!\n",
"\n"
"Project at freq: 1.000e+02\n",
"Project at freq: 1.000e+02\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
"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"
]
}
],
"source": [
"# Run a test\n",
"def fun(u):\n",
" f = problem.fieldsPair(m1d,survey)\n",
" f[src,'e_1d'] = u\n",
" f[src,'b_1d'] = -(m1d.nodalGrad*u)/(1j*simpegem.Utils.EMUtils.omega(src.freq))\n",
" return rx.projectFields(src,m1d,f), lambda t: rx.projectFieldsDeriv(src,m1d,f0,t)\n",
"simpeg.Tests.checkDerivative(fun,u0,num=4,plotIt=False)"
"# print '%s formulation - %s' % (fdemType, comp)\n",
"CONDUCTIVITY = 0.01\n",
"m0 = np.log(np.ones(problem.mesh.nC)*CONDUCTIVITY)\n",
"# mu = np.log(np.ones(problem.mesh.nC)*MU)\n",
"\n",
"if True:\n",
" m0 = m0 + np.random.randn(problem.mesh.nC)*CONDUCTIVITY*1e-1 \n",
"# mu = mu + np.random.randn(prb.mesh.nC)*MU*1e-1\n",
"\n",
"# prb.mu = mu\n",
"# 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)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"problem.getADeriv_m(freq,fields[src,'e_1dSolution'],v)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
@@ -359,64 +468,80 @@
"name": "stdout",
"output_type": "stream",
"text": [
"[ -8.67361738e-19]\n",
"181\n",
"181\n"
"> \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",
"\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[0m\n",
"ipdb> Pex*v\n",
"*** ValueError: dimension mismatch\n",
"ipdb> v.shape\n",
"(180,)\n"
]
}
],
"source": [
"print rx.projectFieldsDeriv(src,m1d,f0,u0)\n",
"print m1d.nF\n",
"print m1d.nN"
"%debug"
]
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# fields._b_1dDeriv_u(src,u0)"
"problem.getA"
]
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "object of type 'FieldsMT' has no len()",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-10-8b883ffdb2f4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msurvey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpred\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf0\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/Utils/CounterUtils.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 81\u001b[0m \u001b[0mcounter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'counter'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 82\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcounter\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mCounter\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcounter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 83\u001b[1;33m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 84\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 85\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpeg/SimPEG/Utils/codeutils.pyc\u001b[0m in \u001b[0;36mrequiresVarWrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 224\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvar\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 225\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mextra\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 226\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 227\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 228\u001b[0m \u001b[0mdoc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequiresVarWrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpeg/SimPEG/Survey.pyc\u001b[0m in \u001b[0;36mdpred\u001b[1;34m(self, m, u)\u001b[0m\n\u001b[0;32m 306\u001b[0m \u001b[0mWhere\u001b[0m \u001b[0mP\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0ma\u001b[0m \u001b[0mprojection\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mfields\u001b[0m \u001b[0monto\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdata\u001b[0m \u001b[0mspace\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 307\u001b[0m \"\"\"\n\u001b[1;32m--> 308\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mu\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mu\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfields\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 309\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[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprojectFields\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[0;32m 310\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpegmt/simpegMT/ProblemMT1D/Problems.pyc\u001b[0m in \u001b[0;36mfields\u001b[1;34m(self, m)\u001b[0m\n\u001b[0;32m 90\u001b[0m '''\n\u001b[0;32m 91\u001b[0m \u001b[1;31m# Set the current model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 92\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcurModel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 93\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 94\u001b[0m \u001b[0mF\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFieldsMT\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[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msurvey\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/Problem.pyc\u001b[0m in \u001b[0;36mcurModel\u001b[1;34m(self, value)\u001b[0m\n\u001b[0;32m 76\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;31m# it is the same!\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 77\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPropMap\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 78\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_curModel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmapping\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 79\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 80\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_curModel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mModels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mModel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmapping\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/PropMaps.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, vec)\u001b[0m\n\u001b[0;32m 254\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 255\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvec\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 256\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPropModel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvec\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 257\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 258\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__contains__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval\u001b[0m\u001b[1;33m)\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/PropMaps.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, propMap, vector)\u001b[0m\n\u001b[0;32m 117\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpropMap\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpropMap\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 118\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvector\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvector\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 119\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvector\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnP\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 120\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 121\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mTypeError\u001b[0m: object of type 'FieldsMT' has no len()"
]
}
],
"outputs": [],
"source": [
"survey.dpred(f0)"
"dMf_dsig = problem.mesh.getFaceInnerProductDeriv(problem.curModel.sigma)(u0) * problem.curModel.sigmaDeriv\n",
"dsig_dm = self.curModel.sigmaDeriv\n"
]
},
{
"cell_type": "raw",
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"problem.mesh.getFaceInnerProductDeriv(problem.curModel.sigma)(u0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"problem.mesh.getEdgeInnerProductDeriv(problem.curModel.sigma)(u0[1::])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
simpegMT/BaseMT.py
+97
View File
@@ -23,3 +23,100 @@ class BaseMTProblem(BaseFDEMProblem):
# Use the forward and devs from BaseFDEMProblem
# Might need to add more stuff here.
def Jvec(self, m, v, f=None):
if f is None:
f = self.fields(m)
self.curModel = m
Jv = self.dataPair(self.survey)
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):
ftype = self._fieldType + 'Solution'
u_src = f[src, ftype]
dA_dm = self.getADeriv_m(freq, u_src, v)
dRHS_dm = self.getRHSDeriv_m(freq, v)
if dRHS_dm is None:
du_dm = dA_duI * ( - dA_dm )
else:
du_dm = dA_duI * ( - dA_dm + dRHS_dm )
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
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)
def Jtvec(self, m, v, f=None):
if f is None:
f = self.fields(m)
self.curModel = m
# Ensure v is a data object.
if not isinstance(v, self.dataPair):
v = self.dataPair(self.survey, v)
Jtv = np.zeros(m.size)
for freq in self.survey.freqs:
AT = self.getA(freq).T
ATinv = self.Solver(AT, **self.solverOpts)
for src in self.survey.getSrcByFreq(freq):
ftype = self._fieldType + 'Solution'
u_src = f[src, ftype]
for rx in src.rxList:
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
dA_dmT = self.getADeriv_m(freq, u_src, dA_duIT, adjoint=True)
dRHS_dmT = self.getRHSDeriv_m(src, dA_duIT, adjoint=True)
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
real_or_imag = rx.projComp
if real_or_imag == 'real':
Jtv += du_dmT.real
elif real_or_imag == 'imag':
Jtv += - du_dmT.real
else:
raise Exception('Must be real or imag')
return Jtv
simpegMT/FieldsMT.py
+82 -9
View File
@@ -9,15 +9,88 @@ from simpegEM.Utils.EMUtils import omega
##############
class FieldsMT(Problem.Fields):
"""Field Storage for a MT survey."""
knownFields = {'b_px': 'F','b_py': 'F', 'e_px': 'E','e_py': 'E','b_1d':'E','e_1d':'F'}
knownFields = {}
dtype = complex
def _b_1dDeriv_u(self,src,v,adjoint=False):
"""
The derivative of b_1d wrt u
"""
nG = self.mesh.nodalGrad
if adjoint:
return - 1./( 1j*omega(src.freq) ) * ( nG.T * v)
return - 1./( 1j*omega(src.freq) ) * ( nG * v)
class FieldsMT_1D(FieldsMT):
"""
Fields storage for the 1D MT solution.
"""
knownFields = {'e_1dSolution':'F'}
aliasFields = {
'e_1d' : ['e_1dSolution','F','_e'],
'e_1dPrimary' : ['e_1dSolution','F','_ePrimary'],
'e_1dSecondary' : ['e_1dSolution','F','_eSecondary'],
'b_1d' : ['e_1dSolution','E','_b'],
'b_1dPrimary' : ['e_1dSolution','E','_bPrimary'],
'b_1dSecondary' : ['e_1dSolution','E','_bSecondary']
}
def __init__(self,mesh,survey,**kwargs):
FieldsMT.__init__(self,mesh,survey,**kwargs)
def _ePrimary(self, eSolution, srcList):
ePrimary = np.zeros_like(eSolution)
for i, src in enumerate(srcList):
ep = src.ePrimary(self.survey.prob)
if ep is not None:
ePrimary[:,i] = ep[:,-1]
return ePrimary
def _eSecondary(self, eSolution, srcList):
return eSolution
def _e(self, eSolution, srcList):
return self._ePrimary(eSolution,srcList) + self._eSecondary(eSolution,srcList)
def _eDeriv_u(self, src, v, adjoint = False):
return None
def _eDeriv_m(self, src, v, adjoint = False):
# assuming primary does not depend on the model
return None
def _bPrimary(self, eSolution, srcList):
bPrimary = np.zeros([self.survey.mesh.nE,eSolution.shape[1]], dtype = complex)
for i, src in enumerate(srcList):
bp = src.bPrimary(self.survey.prob)
if bp is not None:
bPrimary[:,i] += bp[:,-1]
return bPrimary
def _bSecondary(self, eSolution, srcList):
C = self.mesh.nodalGrad
b = (C * eSolution)
for i, src in enumerate(srcList):
b[:,i] *= - 1./(1j*omega(src.freq))
# There is no magnetic source in the MT problem
# S_m, _ = src.eval(self.survey.prob)
# if S_m is not None:
# b[:,i] += 1./(1j*omega(src.freq)) * S_m
return b
def _b(self, eSolution, srcList):
return self._bPrimary(eSolution, srcList) + self._bSecondary(eSolution, srcList)
def _bSecondaryDeriv_u(self, src, v, adjoint = False):
C = self.mesh.nodalGrad
if adjoint:
return - 1./(1j*omega(src.freq)) * (C.T * v)
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
return None
def _bDeriv_u(self, src, v, adjoint=False):
# Primary does not depend on u
return self._bSecondaryDeriv_u(src, v, adjoint)
def _bDeriv_m(self, src, v, adjoint=False):
# Assuming the primary does not depend on the model
return self._bSecondaryDeriv_m(src, v, adjoint)
simpegMT/ProblemMT1D/Problems.py
+14 -12
View File
@@ -3,7 +3,7 @@ from SimPEG import mkvc
from scipy.constants import mu_0
from simpegMT.BaseMT import BaseMTProblem
from simpegMT.SurveyMT import SurveyMT
from simpegMT.FieldsMT import FieldsMT
from simpegMT.FieldsMT import FieldsMT_1D
from simpegMT.DataMT import DataMT
from simpegMT.Utils.MT1Danalytic import getEHfields
import numpy as np
@@ -18,19 +18,19 @@ class eForm_psField(BaseMTProblem):
"""
# From FDEMproblem: Used to project the fields. Currently not used for MTproblem.
_fieldType = 'e'
_fieldType = 'e_1d'
_eqLocs = 'EF'
def __init__(self, mesh, **kwargs):
BaseMTProblem.__init__(self, mesh, **kwargs)
self.fieldsPair = FieldsMT_1D
def getA(self, freq,):
def getA(self, freq):
"""
Function to get the A matrix.
:param float freq: Frequency
:param logic full: Return full A or the inner part
:rtype: scipy.sparse.csr_matrix
:return: A
"""
@@ -54,11 +54,13 @@ class eForm_psField(BaseMTProblem):
"""
dsig_dm = self.curModel.sigmaDeriv
MeMui = self.mesh.getEdgeInnerProduct(1.0/mu_0)
# Need to make the dMf_dsig symmetirc (nN,nN), don't know how to do this
dMf_dsig = self.mesh.getFaceInnerProductDeriv(self.curModel.sigma)(u) * self.curModel.sigmaDeriv
if adjoint:
return 1j * omega(freq) * ( dsig_dm.T * ( dMf_dsig.T * v ) )
return 1j * omega(freq) * ( dMf_dsig * ( dsig_dm * v ) )
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)
def getRHS(self, freq):
"""
@@ -73,7 +75,7 @@ class eForm_psField(BaseMTProblem):
S_e = Src.S_e(self)
return -1j * omega(freq) * S_e
def getRHSderiv_m(self, freq, u, v, adjoint=False):
def getRHSDeriv_m(self, freq, v, adjoint=False):
"""
The derivative of the RHS wrt sigma
"""
@@ -91,7 +93,7 @@ class eForm_psField(BaseMTProblem):
# Set the current model
self.curModel = m
F = FieldsMT(self.mesh, self.survey)
F = FieldsMT_1D(self.mesh, self.survey)
for freq in self.survey.freqs:
if self.verbose:
startTime = time.time()
@@ -110,12 +112,12 @@ class eForm_psField(BaseMTProblem):
# Store the fields
# NOTE: only store
F[Src, 'e_1d'] = e[:,1] # Only storing the yx polarization as 1d
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) )
# b = -( self.mesh.nodalGrad * e )/( 1j*omega(freq) )
# F[Src, 'b_px'] = 0*b[:,0]
F[Src, 'b_1d'] = b[:,1]
# F[Src, 'b_1d'] = b[:,1]
if self.verbose:
print 'Ran for {:f} seconds'.format(time.time()-startTime)
sys.stdout.flush()
simpegMT/SurveyMT.py
+26 -18
View File
@@ -83,7 +83,7 @@ class RxMT(Survey.BaseRx):
"""Component projection (real/imag)"""
return self.knownRxTypes[self.rxType][1]
def projectFields(self, src, mesh, u):
def projectFields(self, src, mesh, f):
'''
Project the fields and return the
'''
@@ -91,8 +91,8 @@ class RxMT(Survey.BaseRx):
if self.projType is 'Z1D':
Pex = mesh.getInterpolationMat(self.locs,'Fx')
Pbx = mesh.getInterpolationMat(self.locs,'Ex')
ex = Pex*mkvc(u[src,'e_1d'],2)
bx = Pbx*mkvc(u[src,'b_1d'],2)/mu_0
ex = Pex*mkvc(f[src,'e_1d'],2)
bx = Pbx*mkvc(f[src,'b_1d'],2)/mu_0
f_part_complex = ex/bx
# elif self.projType is 'Z2D':
elif self.projType is 'Z3D':
@@ -103,14 +103,14 @@ class RxMT(Survey.BaseRx):
Pby = mesh.getInterpolationMat(self.locs,'Fy')
# Get the fields at location
# px: x-polaration and py: y-polaration.
ex_px = Pex*u[src,'e_px']
ey_px = Pey*u[src,'e_px']
ex_py = Pex*u[src,'e_py']
ey_py = Pey*u[src,'e_py']
hx_px = Pbx*u[src,'b_px']/mu_0
hy_px = Pby*u[src,'b_px']/mu_0
hx_py = Pbx*u[src,'b_py']/mu_0
hy_py = Pby*u[src,'b_py']/mu_0
ex_px = Pex*f[src,'e_px']
ey_px = Pey*f[src,'e_px']
ex_py = Pex*f[src,'e_py']
ey_py = Pey*f[src,'e_py']
hx_px = Pbx*f[src,'b_px']/mu_0
hy_px = Pby*f[src,'b_px']/mu_0
hx_py = Pbx*f[src,'b_py']/mu_0
hy_py = Pby*f[src,'b_py']/mu_0
# Make the complex data
if 'zxx' in self.rxType:
f_part_complex = (ex_px*hy_py - ex_py*hy_px)/(hx_px*hy_py - hx_py*hy_px)
@@ -140,7 +140,7 @@ class RxMT(Survey.BaseRx):
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._b_1dDeriv_u(src,v)/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)
# elif self.projType is 'Z2D
elif self.projType is 'Z3D':
pass
@@ -156,7 +156,8 @@ class RxMT(Survey.BaseRx):
###############
### Sources ###
###############
class srcMT(Survey.BaseSrc):
# Note: Should like inheret from FDEM
class srcMT(SrcFDEM): # Survey.BaseSrc):
'''
Sources for the MT problem.
Use the SimPEG BaseSrc, since the source fields share properties with the transmitters.
@@ -205,7 +206,11 @@ class srcMT_polxy_1Dprimary(srcMT):
def bPrimary(self,problem):
# Project ePrimary to bPrimary
# Satisfies the primary(background) field conditions
bBG_bp = (- problem.mesh.edgeCurl * self.ePrimary )/( 1j*omega(freq) )
if problem.mesh.dim == 1:
C = problem.mesh.nodalGrad
elif problem.mesh.dim == 3:
C = problem.mesh.edgeCurl
bBG_bp = (- C * self.ePrimary(problem) )/( 1j*omega(self.freq) )
return bBG_bp
def S_e(self,problem):
@@ -231,15 +236,18 @@ class srcMT_polxy_1Dprimary(srcMT):
def S_eDeriv(self, problem, v, adjoint = False):
# Need to deal with
if problem.mesh.dim == 1:
pass
# Need to use the faceInnerProduct
MsigmaDeriv = problem.mesh.getFaceInnerProductDeriv(problem.curModel.sigma)(self.ePrimary(problem)[:,-1]) * problem.curModel.sigmaDeriv
MsigmaDeriv = ( MsigmaDeriv * MsigmaDeriv.T)**2
if problem.mesh.dim == 2:
pass
if problem.mesh.dim == 3:
MesigmaDeriv = problem.MeSigmaDeriv(self.ePrimary(problem))
MsigmaDeriv = problem.MeSigmaDeriv(self.ePrimary(problem))
if adjoint:
return MesigmaDeriv.T * v
return MsigmaDeriv.T * v
else:
return MesigmaDeriv * v
# Moved the v in front to make the multi work
return MsigmaDeriv * v
##############