Experiement to see if MathJax is rending correctly.

I was specifying the Neo-Euler font, which renders poorly
on nbviewer. This is a test to make sure it works before
checking in all notebooks. (I have to re-execute the notebook)
so that the script from the css file gets embedded into the
notebook).
This commit is contained in:
Roger Labbe committed 2015-07-19 10:58:19 -07:00
1 parent ca88bd82b3
commit 14478ce60a
2 files changed
+45 -45

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+44 -44
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@@ -241,10 +241,10 @@
" },\n",
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
" \"HTML-CSS\": {\n",
" scale:85,\n",
" availableFonts: [\"Neo-Euler\"],\n",
" preferredFont: \"Neo-Euler\",\n",
" webFont: \"Neo-Euler\",\n",
" scale:100,\n",
" availableFonts: [],\n",
" preferredFont:null,\n",
" webFont: \"TeX\",\n",
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
" }\n",
" });\n",
@@ -425,7 +425,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x5878358>"
"<matplotlib.figure.Figure at 0x410f5c0>"
]
},
"metadata": {},
@@ -487,7 +487,7 @@
"\n",
"$$\\mathbf{x} =\\begin{bmatrix}x \\\\ \\dot{x}\\end{bmatrix}$$\n",
"\n",
"We use $\\mathbf{x}$ instead of $\\mu$, but recognize this is the mean of the multivariate Gaussian. It is common to use the transpose notation to write the state variable as $\\mathbf{x} =\\begin{bmatrix}x & \\dot{x}\\end{bmatrix}^\\mathbf{T}$ because the transpose of a row vector is a column vector. This notation is easier to use in running text because it takes less vertical space.\n",
"We use $\\mathbf{x}$ instead of $\\mu$, but recognize this is the mean of the multivariate Gaussian. It is common to use the transpose notation to write the state variable as $\\mathbf{x} =\\begin{bmatrix}x & \\dot{x}\\end{bmatrix}^\\mathsf{T}$ because the transpose of a row vector is a column vector. This notation is easier to use in running text because it takes less vertical space.\n",
"\n",
"Let's code this. FilterPy's `KalmanFilter` class contains a variable `x` for the state variable. Initialization of `x` is as simple as"
]
@@ -888,7 +888,7 @@
"data": {
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x818c128>"
"<matplotlib.figure.Figure at 0x3dc2f60>"
]
},
"metadata": {},
@@ -1414,9 +1414,9 @@
"outputs": [
{
"data": {
"image/png": 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truncated
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x81e3a90>"
"<matplotlib.figure.Figure at 0x7755fd0>"
]
},
"metadata": {},
@@ -1426,7 +1426,7 @@
"data": {
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x82dda58>"
"<matplotlib.figure.Figure at 0x7e96dd8>"
]
},
"metadata": {},
@@ -1473,9 +1473,9 @@
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAE5CAYAAACZGEi2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9aaa8d0>"
"<matplotlib.figure.Figure at 0x7e9df28>"
]
},
"metadata": {},
@@ -1561,7 +1561,7 @@
"data": {
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x80055c0>"
"<matplotlib.figure.Figure at 0x7daed68>"
]
},
"metadata": {},
@@ -1610,7 +1610,7 @@
"data": {
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9b15470>"
"<matplotlib.figure.Figure at 0x7f08518>"
]
},
"metadata": {},
@@ -1673,7 +1673,7 @@
"data": {
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x828a160>"
"<matplotlib.figure.Figure at 0x7d9ed30>"
]
},
"metadata": {},
@@ -1861,9 +1861,9 @@
"outputs": [
{
"data": {
"image/png": 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truncated
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9b9a4e0>"
"<matplotlib.figure.Figure at 0x9163780>"
]
},
"metadata": {},
@@ -1972,7 +1972,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9b76978>"
"<matplotlib.figure.Figure at 0x917fa90>"
]
},
"metadata": {},
@@ -1982,7 +1982,7 @@
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9b76908>"
"<matplotlib.figure.Figure at 0x9197b38>"
]
},
"metadata": {},
@@ -2041,7 +2041,7 @@
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@@ -2071,7 +2071,7 @@
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truncated
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},
"metadata": {},
@@ -2102,7 +2102,7 @@
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truncated
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9a7fa20>"
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},
"metadata": {},
@@ -2207,7 +2207,7 @@
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@@ -2270,7 +2270,7 @@
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truncated
"text/plain": [
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},
"metadata": {},
@@ -2280,7 +2280,7 @@
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truncated
"text/plain": [
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"<matplotlib.figure.Figure at 0x8f6ca58>"
]
},
"metadata": {},
@@ -2324,7 +2324,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x80a7588>"
"<matplotlib.figure.Figure at 0x7f39dd8>"
]
},
"metadata": {},
@@ -2334,7 +2334,7 @@
"data": {
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truncated
"text/plain": [
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},
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@@ -2368,7 +2368,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9b64208>"
"<matplotlib.figure.Figure at 0x9303dd8>"
]
},
"metadata": {},
@@ -2402,7 +2402,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9d04588>"
"<matplotlib.figure.Figure at 0x92b97b8>"
]
},
"metadata": {},
@@ -2439,9 +2439,9 @@
"outputs": [
{
"data": {
"image/png": 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truncated
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9ee0128>"
"<matplotlib.figure.Figure at 0x7f08940>"
]
},
"metadata": {},
@@ -2449,9 +2449,9 @@
},
{
"data": {
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truncated
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAt0AAAEeCAYAAABWo6l1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl8TNf7wPHPneyJrCQRCYLEEsQSsdZaFEVraS1VtLVU\nadH6abUqVEt18dVFdaHqW1RVVReqqBBb7VssRZFEdrInk0wyc39/5JupaYKkWSaJ5/165UXOPffc\n5x6TeObMuecoqqqqCCGEEEIIIcqNxtwBCCGEEEIIUd1J0i2EEEIIIUQ5k6RbCCGEEEKIciZJtxBC\nCCGEEOVMkm4hhBBCCCHKmSTdQgghhBBClDNLcwcghBDi/nL48GEOHDhAeno6hw4d4tVXX6Vbt27m\nDksIIcqVJN1CCCEqTFZWFlu2bGHx4sUAfP/99/Tv35/Lly9Tp04dM0cnhBDlR6aXCCGEqDBXrlxh\nyZIlXL16FYC+ffui1Wo5ePCgmSMTQojyJUm3EEKIChMYGMjBgwdp2LAhAFFRUQD4+/ubMywhhCh3\nimwDL4QQ9zZq1CguXbrEyZMncXBwoFu3bmg0GrKyskhLS6NZs2ZMnjyZLl26lFsMV69eZfbs2dSt\nWxdra2siIyNZvHgxvr6+xTq/SZMmzJs3jx49euDg4MCxY8dYsmQJH3/8MU2aNCm3uO/mySefxN3d\nnaVLl5rl+kIIUVEk6RZCiGI6deoUbdu2Ze7cubzxxhvGclVVmTVrFsuWLWPt2rWMGjWqzK+dnp5O\nYGAgr7/+Ok8//TQAq1at4o033uDs2bM4OTndsw2NxvTDTSsrK5YtW8aUKVPKPN7iWLVqFRcvXuTd\nd981y/WFEKIiyfQSIYQopr179wL585BvpygKEydORFXVcksg3333XdLT0xk7dqyx7MknnyQjI6PY\no8T16tVjwoQJDBo0iJdeeokzZ86YLeHeunUrGo2Gd999l5ycHCIiIswShxBCVBRZvUQIIYopLCyM\nGjVq0KlTp0LHrl+/DoCLi0u5XHvDhg20b98eS8u/f21bW1vTvn17vv32W+bPn3/PNho0aMDnn39e\nLvGVxN69e4mPj+fhhx8mLi6OQ4cO4eXlRf369c0dmhBClBtJuoUQohhUVSUsLIxu3bphYWFR6PiH\nH36IjY0NCxYsKPNrp6enc+XKFR588MFCx+rWrctvv/1GZmYmDg4OZX7tsnb16lUGDRpERkaGsUxR\nFFJTU80YlRBClD9JuoUQohjOnz/PrVu36NOnj0l5bGwsc+bM4caNG4SGhtKxY8cyv3bB1Iui5m07\nOzsDEB8fb1wR5E50Oh1LliwhISEBCwsLrl27xttvv02jRo2KFccPP/xAaGgoZ8+e5auvvuLmzZts\n2rQJRVE4ePAgs2bNol+/fixbtoyEhASysrKIiori66+/NsbesGFD0tLSSnL7QghRLUjSLYQQxVAw\nn/vs2bPMmTOH3NxcDhw4QGRkJCtXruSrr76647lTpkzh1KlTJbrerFmzGDZsGIAxSbW2ti5Ur6As\nOTn5nm3GxcUxevRo6tatC8C6devo2rUrp06dwsPD467n5uTkcODAAT788EOCg4MZM2YMQ4cONW5y\n88477/DMM88wcuRIpk6dSuPGjQEICAhg2bJlzJs3r5h3LoQQ1ZMk3UIIUQxhYWE4OTmxcuVKY1lm\nZib9+vXjP//5D/3797/juStWrCjVtQvmcSuKUuhYTk4OALm5ufds58qVKyZtjBo1ikmTJrFo0SKW\nLVt213P37NlDt27dUFWVv/76i969ezNz5kyTGG/dusWoUaOMCTfkr5CSmJh4z9iEEKK6k9VLhBCi\nGPbu3UuHDh1MyhwcHBg5ciS7du1iz5495XZtd3f3Ox7LzMwEwNHR8Z7t/DNp12g01KpVix9//PGe\n57Zs2ZKHHnqI06dPk5KSwvTp002OHzlyhHbt2plMr0lPT+fChQu0atXqnu0LIUR1J0m3EELcw6VL\nl4iPj6dz586FjkVGRgKQlJRUbtf39PREUZQip5BkZmaiKMo9N8jp0aMHAQEBxpHxAgaDgfj4+HvG\nUKdOHWxsbNi9ezd2dnaF3oCEhobSs2dPk7KtW7diMBh4+OGH79m+EEJUdzK9RAgh7qFgPndRSXdo\naChw923MJ0+ezIkTJ0p0zVdeecU4p9ve3p6goCBjgn+7K1eu4Ofnd8+VS44fP17kiHliYiINGjQo\ndly7d++mc+fOJksXnjt3jsTExEJJ94YNG+jRowdeXl5ERkbi7e1d5MovQghxP5CkWwgh7iEsLAwL\nC4siVyYJDw9HURTjyiEJCQk4OTlha2trrPPZZ5+VOoZBgwaxfPlyVFU1ThNJSUnh5MmTvPTSSyZ1\nr1+/jpubm8lqJ/379+ezzz7DxsbGWHbq1Cl0Oh1jxowpVgx6vZ59+/Yxe/Zsk/Ldu3djZWXFAw88\nYCxLTU3l119/5csvvwRg8eLFfPLJJyW7aSGEqEZkeokQQtyFqqrs2bOHZs2aFTlvumC5vYIt1l9+\n+WX0en2ZxzF58mRyc3NZtWqVsezTTz+lVq1avPzyy8ayv/76C39/f9q3b29y/uzZs5k0aRJardZ4\nX0uXLqVz586Fkug7OXbsGOnp6fTo0cOkfPfu3bRv3x57e3uTOHJzcxk0aBAnT54kICCgyAdBhRDi\nfiEj3UIIUYSrV68yefJkoqOjiYmJISkpiZ49ezJy5EgmT55srLdu3TpmzJjB1KlTURSFyZMnl8sm\nNR4eHoSFhfHKK69w9uxZcnNziYqK4vfffzd5M+Di4kLdunULTXdp164d06dPZ9y4cdjZ2ZGWlkZg\nYCArV67EysqqWDHExsbSsmXLQvO5b968ybhx40zKWrduzfDhw3n55ZepXbu2LBkohLjvKaqqquYO\nQgghhBBCiOpMppcIIYQQQghRziTpFkIIIYQQopxJ0i2EEEIIIUQ5q/AHKVNTUyv6kkIIIYQQQpQZ\nZ2fnEp8jI91CCCGEEEKUM0m6hRBCCCGEKGdmXaf73wzN34+OHTsG5K+zK+5N+qvkpM9KRvqr5KTP\nSkb6q+Skz0pO+qxkSjtFWka6hRBCCCGEKGeSdAshhBBCCFHOJOkWQgghhBCinBUr6V68eDHBwcE4\nOzvj4eHB4MGDOXfuXKF68+fPx9vbG3t7e3r27Mn58+fLPGAhhBBCCCGqmmIl3Xv37mXatGkcOnSI\n3bt3Y2lpSe/evUlOTjbWWbJkCUuXLuXjjz/m6NGjeHh40KdPHzIyMsoteCGEEEIIIaqCYq1esn37\ndpPvv/76a5ydnTl48CAPP/wwqqqybNky5syZw5AhQwBYs2YNHh4erF+/nkmTJpV95EIIIYQQQlQR\n/2rJwLS0NAwGA66urgBcu3aN+Ph4+vbta6xja2tLt27dOHjwoCTdQlQReoOeHH0OeoMeS40ldlZ2\n5g5JCFGJqaqKTqdDVVVzh0L9+vUByM7ONnMkVYf02d8URcHa2hpFUcrtGv8q6Z4+fTpt2rShU6dO\nAMTFxQHg6elpUs/Dw4OYmJhShiiEKA86vY7I1Eii06LZH72fFF0K2zK3YVANxjqutq4M8B+Af01/\nM0YqhKiMDAYDOTk5WFtbY2FhYe5wsLW1NXcIVY702d/0ej3Z2dnY2Nig0ZTPOiMlTrpffPFFDh48\nyP79+4v1buBudQoWZRfFI/1VMtJfRYvJiuFi6kWuZ1wnT83DYFAw5FmQcFODd24aVhordAZdfl1i\nyEzIpG+dvvdo9f4kr7GSkz4rmcrcX3Xq1MHLy6tcRwaFqCgWFhbY2toSGxt7xwFjf//SDUCVKOme\nOXMmGzduJDQ0FF9fX2N57dq1AYiPj8fHx8dYHh8fbzwmhDAvg2pgX8I+/kz901hW08qTXR89jj5K\nwa3/SRo0iSZOG2dMuq00VgQ4B5grZCFEJWZlZSUJt6hWFEXBysqq3NovdtI9ffp0vvvuO0JDQ2nc\nuLHJsQYNGlC7dm127NhBUFAQkD8/aP/+/bz33nt3bFO2HS0e2aa1ZKS/ipakTWLb4W3UcagDgD5P\nw89vD+f0vgBslSwMf+whzyMbX18X2jRtQEvPlgTXCcbRxtHMkVc+8horOemzkqkK/SXzgEV15Ojo\neMefu9JuA1+spHvq1KmsXbuWLVu24OzsbJzD7ejoiIODA4qiMGPGDBYtWkTTpk3x9/fnzTffxNHR\nkdGjR5cqQCFE2XC1daVZrWZcvHmRvFwN3785jAthAdjY5zB+2goiMhyYP3EAdZ3qUrtGbRnBEkII\nIcpQsZLuFStWoCgKDz74oEn5/PnzmTdvHgCzZ89Gq9UydepUkpOT6dixIzt27MDBwaHsoxZClIyq\nomzdyohrCaQ/M4eRI+BCmDXOzirbt1tjadmd48cdae/dxNyRCiGEENVSsZJug8Fw70pASEgIISEh\npQpICFHGIiLgySdh3z6ybZwZ+eNEtv1ui6sr7NypEBQEx45BUFC6uSMVQgghqq3yWRNFCFE5fPst\naqtWsG8f6bW86et9iG2/2+LooiNk9R607vu5mXXT3FEKIUSl8dVXX6HRaNBoNOzfv7/IOn5+fmg0\nGnr27FnB0YnbHTx4kAULFpR6rnVFkaRbiGpIm6vlxuszYORIlNRUTnRsTTO3UPZdbYaDawZjlq4k\n2XkPu67uYvXJ1ehVvblDFkKISsXOzo7169cXKv/jjz+4evUqtra28uyLmUnSLYQwm1x9Ltsub+P9\nQ++zqTmku9Vgy9TBjMjdQvQlf1xrZTN31e94NEgwnqOiopFfBUIIYaJ///5899135OXlmZSvX7+e\npk2b0qhRIzNFVjYyMzPNHUKZqQw7ohaH/E8rRDWy6fwmjkQfIc+QR0ptF779cTHzzLine truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x81adeb8>"
"<matplotlib.figure.Figure at 0x8f6f2e8>"
]
},
"metadata": {},
@@ -2559,9 +2559,9 @@
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAt0AAADZCAYAAAD8OYzNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAt0AAADZCAYAAAD8OYzNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd8VfX9/59373uzd0gICZAwZYkoCCoKrmqd8HW1Dmr9\nOlr5ifq1VWuVUi1StdpatGoVR92rCspShoAMgUAGCdl73j3P74/rPeSSAAkQlp8nj/O45HM+95zP\nOblJXp/3eX3eb4UkSRICgUAgEAgEAoGg31Ae7wEIBAKBQCAQCASnOkJ0CwQCgUAgEAgE/YwQ3QKB\nQCAQCAQCQT8jRLdAIBAIBAKBQNDPCNEtEAgEAoFAIBD0M0J0CwQCgUAgEAgE/Yz6eA9AIBAIBD9N\nvvvuO9asWYPdbmfdunU8+OCDTJky5XgPSyAQCPoFIboFAoFAcMxxuVx8+OGHzJ8/H4D33nuPmTNn\nUlJSQlpa2nEenUAgEBx9hL1EIBAIBMec0tJSFixYQFlZGQDnn38+brebtWvXHueRCQQCQf8gRLdA\nIBAIjjkjR45k7dq15OTkAFBVVQVAXl7e8RyWQCAQ9BsKUQZeIBAIDsysWbMoLi5my5YtmEwmpkyZ\nglKpxOVy0dnZSX5+PnPmzOHMM8/stzEMGTKE3//+90ydOhWTycSmTZtYsGABzz33HEOGDJH7lZWV\ncd9995GZmYlWq6WyspL58+eTnZ3d7Zh96XssuP7660lMTGThwoXH5fwCgUDQ70gCgUAgOChbtmyR\nFAqF9Lvf/S6qPRQKSb/97W8lpVIpLVmypN/Or1AoojatVis9//zzUX06Ozul7Oxs6aWXXpLbFi9e\nLA0YMEDq6Og47L7HgsWLF0tz58495ucVCASCY4mwlwgEAsEhWLVqFRD2HXdFoVBw6623IkkSTz75\nZL+df8CAAdxyyy1ccskl3Hvvvfzwww/cfvvtUX2efPJJ7HY7N9xwg9x2/fXX43A4ukWP+9K3v/ns\ns89QKpU8+eSTeL1eKioqjun5BQKB4FghRLdAIBAcgtWrV2M2mznjjDO67du7dy8AMTEx/Xb+gQMH\n8uKLL/LRRx/x5JNPRllKIrz11ltMmDABtXpfUiqtVsuECRN4++23D7tvf7Jq1SoaGhq48MILqa+v\n5/PPP6euru6YnV8gEAiOJSJloEAgEBwESZJYvXo1U6ZMQaVSddv/zDPPoNPpePTRR4/D6MLY7XZK\nS0s599xzu+3LzMzkyy+/xOl0YjKZ+tS3PykrK+OSSy7B4XDIbQqFgo6Ojn49r0AgEBwvhOgWCASC\ng1BYWEhLSwvTp0+Paq+rq+OBBx6gurqaFStWMHHixH4bg8/nY8GCBTQ2NqJSqSgvL+dPf/oTgwYN\nApAtGVartdt7bTYbAA0NDeTk5PSp78H44IMPWLFiBdu3b+eVV16hubmZd999F4VCwdq1a5k7dy4z\nZsxg0aJFNDY24nK5qKqq4t///jdWq5WcnBw6OzsP/6YIBALBSYYQ3QKBQHAQIn7u7du388ADD+D3\n+1mzZg2VlZUsXryYV1555YDvvf3229m6dWufzjd37lyuuOKKqLb6+npmz55NZmYmAG+88QaTJ09m\n69atJCUlyeJVq9V2O16kra2tDaBPfQ+E1+tlzZo1PPPMM4wfP57rrruOn//853Khmz//+c/cfPPN\nXHvttdxxxx0MHjwYgIKCAhYtWsTvf//73t0MgUAgOIUQolsgEAgOwurVq7FarSxevFhuczqdzJgx\ng6effpqZM2ce8L0vvPDCURlDaWkpCoVC/nrWrFncdtttPPHEEyxatEj2ZnftE8Hr9QLg9/sB+tT3\nQKxcuZIpU6YgSRJ79uzhvPPO4ze/+Y28X61W09LSwqxZs2TBDaDRaGhqaurVNQsEAsGphlhIKRAI\nBAdh1apVnH766VFtJpOJa6+9lq+++oqVK1f2+xj2F8hKpZKEhAQ++ugjABITEw/4XqfTCYDFYulz\n3wMxYsQILrjgArZt20Z7ezt333131P4NGzYwbty4KMuN3W5n165djBo16qDHFggEglMVIboFAoHg\nABQXF9PQ0MCkSZO67ausrASgtbW1X8cwdepUCgoK5Ch0hFAoRENDAwDJyckoFIoebSFOpxOFQiEX\nvelL3wORlpaGTqdj+fLlGAyGbpOSFStWMG3atKi2zz77jFAoxEUXXXSoSxYIBIJTEmEvEQgEggMQ\n8XP3JLpXrFgBHLxs+Zw5c9i8eXOfznn//fdHebq///77HqPTTU1NDBw4EACj0cjYsWPliUBXSktL\nyc3NlbOR9KXvoVi+fDmTJk2KSj24c+dOmpqauonut956i6lTp5KamkplZSXp6ek9ZoMRCASCUxUh\nugUCgeAArF69GpVK1WNmkh07dqBQKOQsH42NjVitVvR6vdznH//4xxGPYebMmfzjH/9Ap9PJbVu3\nbsXn83HdddfJbZdccgl/+9vfkCRJtqO0t7ezZcsW7r333qhj9qXvgQgGg3zzzTfcd999Ue3Lly9H\no9Fw1llnyW0dHR3897//5eWXXwZg/vz5PP/88324CwKBQHDyI+wlAoFA0AOSJLFy5Ury8/N79DhH\n0vUpleFfo/PmzSMYDB71cdx3333cdtttuN1ueVwLFy5k0qRJUYJ3zpw5+P1+XnrpJbnt73//OwkJ\nCcybNy/qmH3peyA2bdqE3W5n6tSpUe3Lly9nwoQJGI1GuW3Pnj34/X4uueQStmzZQkFBQY8LOQUC\ngeBURkS6BQKBoAtlZWXMmTOHmpoaamtraW1tZdq0aVx77bXMmTNH7vfGG29wzz33cMcdd6BQKJgz\nZ06/FJQZN24cd999NzfeeCMGg4HOzk5GjhzJ4sWL0Wg0cr+kpCRWr17N/fffz/bt2/H7/VRVVfH1\n1193mzT0pe+BqKurY8SIEd383M3Nzdx4441RbaNHj+bKK69k3rx5pKSkiJSBAoHgJ4lCkiTpeA9C\nIBAIBAKBQCA4lRH2EoFAIBAIBAKBoJ8RolsgEAgEAoFAIOhnhOgWCAQCgUAgEAj6mWO+kLKjo+NY\nn1IgEAgEAoFAIDhq2Gy2Pr9HRLoFAoFAIBAIBIJ+RohugUAgEAgEAoGgnzmueboPJzT/U2TTpk1A\nOF+v4NCI+9V3xD3rG+J+9R1xz/qGuF99R9yzviPuWd84Uou0iHQLBAKBQCAQCAT9jBDdAoFAIBAI\nBAJBP9Nr0b169WouvfRSMjIyUCqVvPrqq936PPLII6Snp2M0Gpk2bRqFhYVHdbACgUAgEAgEAsHJ\nSK9Ft9PpZOTIkfz1r3/FYDCgUCii9i9YsICFCxfy3HPPsXHjRpKSkpg+fToOh+OoD1ogEAgEAoFA\nIDiZ6PVCypkzZzJz5kwAbrrppqh9kiSxaNEiHnjgAS6//HIAXn31VZKSkliyZAm33Xbb0RuxQCAQ\nHGUkScIX9OH0O3H6nDj9TrwBL/6Qn0AogD/44+uPX++q30VAClC6o7TbvmAoiE6tw6gxYtKYwq/a\n8GuGNYMkU9LxvlyBQCAQHAeOSvaS8vJyGhoaOP/88+U2vV7PlClTWLt2rRDdAoHgmOMP+nH5XVFC\nuuvr/vsCoUCvj13bWQuAu9nd53Elm5KZnDWZ4UnD+/xegeBEQ5IkfD4fkiQd76GQlZUFgMfjOc4j\nOXkQ92wfCoUCrVbbzclxNDkqoru+vh6A5OTkqPakpCRqa2sP+L5IqhpB7xD3q2+I+9V3TvR7JkkS\n3pAXh9+BM+CUN1fQhSfgwR104w668QQ9+EP+Ph1brVCjV+sxqAwYVAa0Si1qpRqVQoVaqUatCG8q\nhYrc5Nxwu6RGpVTJ7ZH+3qAXT9Ajb+6gG1fARYWzgtpQLTv27OD6nOtRK49r1tbjwon+GTvROJHv\nl1qtJjMzE5vNhlp9/D/Ler3+eA/hpEPcs30EAgFaW1upqqoiEOg5CJOXl3dE5+j3n5L+nDEIBIJT\nB0mScAfdUWLaEXCEo9Fd2oJSsFfHUyqUGFQG9Cr9vle1QRbVXb/Wq/RolJp+vT5v0Iu33kuVs4qg\nFESpEMmjBCc3SUlJxMXFib/zglMCtVpNXFwcXq/3oAHjIzrH0ThISkoKAA0NDWRkZMjtDQ0N8r6e\nEMnYe4dIXt83xP3qO8finvmCPjo8HXR4O+jwdNDp7ZT/H3mNEtTqHzc9KFBg/vGfXq3HqrPKm0Vr\nwaKzYNKYMGlN8qtOpTuoGJAkiUAogC/owxv04gv6um3ewL72kBRCoVCgVCgpKS5BgYKC/AJMGhM2\nvQ2bzoZVZ0Wjihbv7Z52vqv+js11mwnaguTE53BF/hXkxR9ZxORkQ/xc9o2T4X55PB4huAWnFAqF\ngri4ONLS0nrcf6TFcY6K6B44cCApKSksXbqUsWPHAuEfxm+//ZannnrqaJxCIBCc4HgCHtrcbbR5\n2mhzt3UT1O7Aof3PRo0xSlDvv1m0FnRq3UGPIUkSTr+TNncbDp8Du8+O3WuPenX4HDh8DkJS6LCu\ntbYlHAWpLeseDcmyZTEyeSTxxng21GxgV9MuJMJ+1+yYbC4dcilxhrjDOq9AIBAITl56LbqdTicl\nJSUAhEIhKioq2Lp1K/Hx8WRmZnLPPffwxBNPMHToUPLy8vjjH/+IxWJh9uzZ/TZ4gUBw7AiGgrR7\n2mn3tMvCOvLa7mk/pKhWK9VYdVZsOpscGe76atVZ0aq0B3y/LKYdbd1EtN1rlwV2X8S0WqlGq9Ki\nU+nQqrTdNp16X7tSoUSSJEJSiF2eXYQIkZeZh8PnkCcWdY46vq38lvd3vY9RYyQ/MR+VQsXwpOFM\nzJhIqiW1T/dcIBAIBKcOvRbdGzdu5JxzzgHC4feHH36Yhx9+mJtuuomXX36Z++67DLine truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x80d0208>"
"<matplotlib.figure.Figure at 0x7e2a7b8>"
]
},
"metadata": {},
@@ -2694,9 +2694,9 @@
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEPCAYAAACTNwgMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x81f74a8>"
"<matplotlib.figure.Figure at 0x7d9ca90>"
]
},
"metadata": {},
@@ -2745,9 +2745,9 @@
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEPCAYAAACTNwgMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEPCAYAAACTNwgMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x8224400>"
"<matplotlib.figure.Figure at 0x7e98978>"
]
},
"metadata": {},
@@ -2786,9 +2786,9 @@
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAt0AAAEWCAYAAAC68CsYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAt0AAAEWCAYAAAC68CsYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9a702b0>"
"<matplotlib.figure.Figure at 0x6b18a20>"
]
},
"metadata": {},
@@ -2852,9 +2852,9 @@
"outputs": [
{
"data": {
"image/png": 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truncated
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x808c6d8>"
"<matplotlib.figure.Figure at 0x7f511d0>"
]
},
"metadata": {},
+1 -1
View File
@@ -213,7 +213,7 @@
inlineMath: [ ['$','$'], ["\\(","\\)"] ],
displayMath: [ ['$$','$$'], ["\\[","\\]"] ]
},
displayAlign: 'left', // Change this to 'center' to center equations.
displayAlign: 'center', // Change this to 'center' to center equations.
"HTML-CSS": {
scale:100,
availableFonts: [],