mirror of
https://github.com/wassname/simpeg.git
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Minor change
This commit is contained in:
D Fournier committed
1 parent
d2db56337a
commit
000e227ea4
1 file changed
+31
-29
@@ -43,7 +43,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 133,
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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@@ -53,15 +53,19 @@
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"output_type": "stream",
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"text": [
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"Using matplotlib backend: nbAgg\n",
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"Populating the interactive namespace from numpy and matplotlib\n"
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"Populating the interactive namespace from numpy and matplotlib\n",
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"Efficiency Warning: Interpolation will be slow, use setup.py!\n",
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"\n",
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" python setup.py build_ext --inplace\n",
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" \n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"WARNING: pylab import has clobbered these variables: ['linalg', 'beta', 'inv']\n",
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"`%matplotlib` prevents importing * from pylab and numpy\n"
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"C:\\Users\\dominiquef.MIRAGEOSCIENCE\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead.\n",
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" \"You should import from ipykernel or jupyter_client instead.\", ShimWarning)\n"
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]
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}
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],
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@@ -74,7 +78,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 135,
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"execution_count": 2,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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@@ -164,7 +168,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 136,
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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@@ -201,7 +205,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 137,
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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@@ -990,7 +994,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 138,
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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@@ -1734,7 +1738,7 @@
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{
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"data": {
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"<img 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truncated
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
@@ -1746,10 +1750,10 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.colorbar.Colorbar instance at 0x00000000395D3E48>"
|
||||
"<matplotlib.colorbar.Colorbar instance at 0x00000000166F7A88>"
|
||||
]
|
||||
},
|
||||
"execution_count": 138,
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1798,7 +1802,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 141,
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -1847,7 +1851,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 142,
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -1861,17 +1865,15 @@
|
||||
"=============================== Projected GNCG ===============================\n",
|
||||
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
|
||||
"-----------------------------------------------------------------------------\n",
|
||||
" 0 1.00e+04 6.75e+04 3.37e-04 6.75e+04 8.30e+01 0 \n",
|
||||
" 1 5.00e+03 2.77e+03 3.19e-02 2.93e+03 6.90e+01 0 \n",
|
||||
" 2 2.50e+03 4.03e+02 5.53e-02 5.41e+02 7.04e+01 0 \n",
|
||||
" 3 1.25e+03 2.15e+02 5.75e-02 2.87e+02 6.99e+01 0 Skip BFGS \n",
|
||||
" 4 6.25e+02 2.00e+02 5.82e-02 2.37e+02 7.31e+01 0 Skip BFGS \n",
|
||||
" 0 1.00e+04 6.72e+04 3.37e-04 6.72e+04 8.30e+01 0 \n",
|
||||
" 1 5.00e+03 2.72e+03 3.17e-02 2.88e+03 6.90e+01 0 \n",
|
||||
" 2 2.50e+03 3.73e+02 5.49e-02 5.10e+02 7.03e+01 0 \n",
|
||||
"------------------------- STOP! -------------------------\n",
|
||||
"1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 6.7516e+03\n",
|
||||
"1 : |xc-x_last| = 3.1475e-03 <= tolX*(1+|x0|) = 1.0111e-01\n",
|
||||
"0 : |proj(x-g)-x| = 7.3120e+01 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 7.3120e+01 <= 1e3*eps = 1.0000e-02\n",
|
||||
"0 : maxIter = 10 <= iter = 5\n",
|
||||
"1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 6.7163e+03\n",
|
||||
"1 : |xc-x_last| = 6.3549e-03 <= tolX*(1+|x0|) = 1.0111e-01\n",
|
||||
"0 : |proj(x-g)-x| = 7.0322e+01 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 7.0322e+01 <= 1e3*eps = 1.0000e-02\n",
|
||||
"0 : maxIter = 10 <= iter = 3\n",
|
||||
"------------------------- DONE! -------------------------\n"
|
||||
]
|
||||
}
|
||||
@@ -1889,7 +1891,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 143,
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -2633,7 +2635,7 @@
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
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truncated
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
@@ -2673,7 +2675,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 144,
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -3417,7 +3419,7 @@
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<img src=\"data:image/png;base64,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 truncated
|
||||
"<img src=\"data:image/png;base64,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 truncated
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
@@ -3429,10 +3431,10 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.colorbar.Colorbar instance at 0x000000003D2A8FC8>"
|
||||
"<matplotlib.colorbar.Colorbar instance at 0x000000001BAAA2C8>"
|
||||
]
|
||||
},
|
||||
"execution_count": 144,
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
||||
Reference in new issue
Block a user