Reran to ensure uptodate with current python environment
Python 3.6.5
This commit is contained in:
Roger Labbe committed
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commit
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24 files changed
+955
-1054
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+62
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@@ -246,11 +246,14 @@
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" },\n",
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" displayAlign: 'center', // Change this to 'center' to center equations.\n",
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" \"HTML-CSS\": {\n",
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" scale:95,\n",
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" scale: 95,\n",
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" webFont: \"TeX\",\n",
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" },\n",
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" \"disabled\": false\n",
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" }\n",
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" });\n",
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"</script>\n"
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@@ -556,7 +559,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[ 1.0 2.0 3.0]\n"
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"[1. 2. 3.]\n"
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]
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}
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],
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@@ -719,20 +722,20 @@
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"output_type": "stream",
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"text": [
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"abddition:\n",
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" [[ 2.0 4.0]\n",
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" [ 6.0 8.0]]\n",
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" [[2. 4.]\n",
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" [6. 8.]]\n",
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"\n",
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"element-wise multiplication\n",
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" [[ 1.0 4.0]\n",
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" [ 9.0 16.0]]\n",
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" [[ 1. 4.]\n",
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" [ 9. 16.]]\n",
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"\n",
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"multiplication\n",
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" [[ 7.0 10.0]\n",
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" [ 15.0 22.0]]\n",
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" [[ 7. 10.]\n",
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" [15. 22.]]\n",
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"\n",
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"dot is also a member of np.array\n",
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" [[ 7.0 10.0]\n",
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" [ 15.0 22.0]]\n"
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" [[ 7. 10.]\n",
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" [15. 22.]]\n"
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]
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}
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],
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@@ -778,16 +781,16 @@
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"output_type": "stream",
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"text": [
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"transpose\n",
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" [[ 1.0 3.0]\n",
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" [ 2.0 4.0]]\n",
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" [[1. 3.]\n",
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" [2. 4.]]\n",
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"\n",
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"NumPy ninverse\n",
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" [[ -2.0 1.0]\n",
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" [ 1.5 -0.5]]\n",
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" [[-2. 1. ]\n",
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" [ 1.5 -0.5]]\n",
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"\n",
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"SciPy inverse\n",
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" [[ -2.0 1.0]\n",
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" [ 1.5 -0.5]]\n"
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" [[-2. 1. ]\n",
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" [ 1.5 -0.5]]\n"
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]
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}
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],
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@@ -815,17 +818,17 @@
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"output_type": "stream",
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"text": [
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"zeros\n",
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" [ 0.0 0.0 0.0 0.0 0.0 0.0 0.0]\n",
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" [0. 0. 0. 0. 0. 0. 0.]\n",
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"\n",
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"zeros(3x2)\n",
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" [[ 0.0 0.0]\n",
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" [ 0.0 0.0]\n",
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" [ 0.0 0.0]]\n",
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" [[0. 0.]\n",
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" [0. 0.]\n",
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" [0. 0.]]\n",
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"\n",
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"eye\n",
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" [[ 1.0 0.0 0.0]\n",
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" [ 0.0 1.0 0.0]\n",
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" [ 0.0 0.0 1.0]]\n"
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" [[1. 0. 0.]\n",
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" [0. 1. 0.]\n",
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" [0. 0. 1.]]\n"
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]
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}
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],
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@@ -850,8 +853,8 @@
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{
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"data": {
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"text/plain": [
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"array([ 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9,\n",
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" 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9])"
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"array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1,\n",
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" 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9])"
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]
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},
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"execution_count": 14,
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@@ -871,9 +874,9 @@
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{
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"data": {
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"text/plain": [
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"array([ 0.0, 0.105, 0.211, 0.316, 0.421, 0.526, 0.632, 0.737,\n",
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" 0.842, 0.947, 1.05, 1.16, 1.26, 1.37, 1.47, 1.58,\n",
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" 1.68, 1.79, 1.89, 2.0])"
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"array([0. , 0.105, 0.211, 0.316, 0.421, 0.526, 0.632, 0.737, 0.842,\n",
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" 0.947, 1.053, 1.158, 1.263, 1.368, 1.474, 1.579, 1.684, 1.789,\n",
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" 1.895, 2. ])"
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]
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},
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"execution_count": 15,
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@@ -900,12 +903,22 @@
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x21b07a17780>]"
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"[<matplotlib.lines.Line2D at 0x2251b616eb8>]"
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]
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},
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"execution_count": 16,
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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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"data": {
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"image/png": 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truncated
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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@@ -927,10 +940,19 @@
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"metadata": {},
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"outputs": [
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{
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"data": {
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||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plt.plot(np.arange(0,1, 0.1), [1,4,3,2,6,4,7,3,4,5]);"
|
||||
]
|
||||
@@ -954,9 +976,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# your solution"
|
||||
@@ -980,9 +1000,9 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[ 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]\n",
|
||||
"[ 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]\n",
|
||||
"[ 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]\n"
|
||||
"[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]\n",
|
||||
"[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]\n",
|
||||
"[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1008,8 +1028,8 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[ 0.1 0.2 0.3]\n",
|
||||
"[ 0.4 0.5 0.6]\n"
|
||||
"[0.1 0.2 0.3]\n",
|
||||
"[0.4 0.5 0.6]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1135,7 +1155,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.4"
|
||||
"version": "3.6.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
+70
-89
@@ -331,9 +331,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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\n",
|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70b76d7f0>"
|
||||
"<Figure size 576x144 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -379,9 +379,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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\n",
|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70baa1630>"
|
||||
"<Figure size 576x144 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -408,9 +408,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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\n",
|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70bb49fd0>"
|
||||
"<Figure size 576x144 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -443,7 +443,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Average of measurements is 164.9808\n"
|
||||
"Average of measurements is 165.0563\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -472,7 +472,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Average of measurements is 164.9809\n"
|
||||
"Average of measurements is 165.1012\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -501,9 +501,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70a8144a8>"
|
||||
"<Figure size 576x252 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -533,9 +533,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70bca20b8>"
|
||||
"<Figure size 576x180 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -560,9 +560,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70bba2d30>"
|
||||
"<Figure size 576x180 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -589,9 +589,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70bd78ac8>"
|
||||
"<Figure size 576x180 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -618,9 +618,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70bd9f240>"
|
||||
"<Figure size 576x180 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -651,9 +651,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70bccf5f8>"
|
||||
"<Figure size 576x180 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -678,9 +678,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70bbc3b38>"
|
||||
"<Figure size 576x180 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -724,9 +724,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70bbfcb38>"
|
||||
"<Figure size 576x180 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -823,9 +823,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70bae1630>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -857,9 +857,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70bb673c8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -902,9 +902,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70ce494e0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -988,9 +988,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70ce528d0>"
|
||||
"<Figure size 288x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1128,9 +1128,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70ce6ac50>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1227,9 +1227,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70ce12cf8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1262,9 +1262,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70baa7e48>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1312,9 +1312,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70be2eda0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1371,9 +1371,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70bad6b38>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1432,9 +1432,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70bbc3c18>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1507,9 +1507,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70be12908>"
|
||||
"<Figure size 576x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1546,9 +1546,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70bd08710>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1588,9 +1588,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70bd39358>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1633,9 +1633,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70cf48320>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1678,28 +1678,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "3c6019dc7f974a9a8191111895c6872d",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/html": [
|
||||
"<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
|
||||
"<p>\n",
|
||||
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
|
||||
" that the widgets JavaScript is still loading. If this message persists, it\n",
|
||||
" likely means that the widgets JavaScript library is either not installed or\n",
|
||||
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
|
||||
" Widgets Documentation</a> for setup instructions.\n",
|
||||
"</p>\n",
|
||||
"<p>\n",
|
||||
" If you're reading this message in another frontend (for example, a static\n",
|
||||
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
|
||||
" it may mean that your frontend doesn't currently support widgets.\n",
|
||||
"</p>\n"
|
||||
],
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"interactive(children=(FloatSlider(value=0.0, continuous_update=False, description='x', max=200.0, min=-200.0), FloatSlider(value=5.0, continuous_update=False, description='dx', max=50.0, min=-50.0), FloatSlider(value=0.1, continuous_update=False, description='g', max=2.0, min=0.01, step=0.02), FloatSlider(value=0.02, continuous_update=False, description='h', max=0.5, step=0.01), Output()), _dom_classes=('widget-interact',))"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1749,9 +1730,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAAD1CAYAAAAf6YHTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzsnXd4VEXXwH93UwhptAQCBAhC6CX0wCtgeAVBikgv0uUTaSKgoIIoosIrihQJgoA0kd6bSJEmEHqRpkBCCCWUBAipu+f7I9lrGiXJJpuE+T3PfbI7M3fm7OTeOTNnzsxoIoJCoVAoFNbGYG0BFAqFQqEApZAUCoVCkU1QCkmhUCgU2QKlkBQKhUKRLVAKSaFQKBTZAltrC5Aa4eHhyvVPoVAocjn58uXTEn9XIySFQqFQZAuUQlIoFApFtiBbmuwSky9fvnTdd+TIEQBq165tSXFyNKpOUqLqJCWqTlKi6iQl6a2T8PDwJ8apEZJCoVAosgVKISkUCoUiW5DtTXYKhUIRFxfH8ePHsbGxoVixYtjY2GRp+c7OzgCEhoZmabnZmeepE4PBQMGCBdE07YlpEqMUkkKhyNasXbuWoUOH0rx5c/r06cPNmzcpVqwYhQsXzjIZHB0dAXBycsqyMrM7z1MnUVFR3Lt3j0KFCj1XnkohKRSKbMvevXtp3749JpOJWrVq4eLiQlRUFEFBQdjY2Dx3Q6ewDg4ODjx8+PC506s5JIVCkW353//+h8lkYsiQIbz55ptUrlyZEiVKAHDjxg3U8Tm5C6WQFApFtuXw4cMADB8+HE3T0DQNd3d3NE0jKioKk8mUpfJMmzaNihUrUqBAASZOnAjAZ599xuTJkwH4+eefCQkJyVKZchNKISkUimxLgQIFALh48aIeFhMTg4joCiormTlzJps3b+b+/fuMHj06RXx6FFJcXJylxMvxKIWkUCiyLd27dwegb9++XLp0ibt37/LPP/8AULBgQQyGrGvChg4dyuXLl2nTpg1Tpkxh8ODBSeJXrlzJkSNH6N69Oz4+PkRGRnL06FEaN25MrVq1eO2117hx4wYAr7zyCh9//DGNGzdm6tSpWfYbsjvKqUGhUGRbRowYwW+//ca+ffs4dOgQ9vb2QPxk+ahtN9C0mxYra9k79Z8aP23aNHbs2MGuXbvYuHFjivgOHTowY8YMJk+eTO3atYmNjWXIkCGsW7cOd3d3li1bxieffMK8efMACAsL448//rCY/LkBpZAUCkW2xdHRkd9//50lS5bg4uKCq6sr+fLlw83NDW3/YWuL91QuXLjAmTNnaNq0KQBGo5GiRYvq8Z07d7aWaNkWpZAUCkW2Jk+ePPTt25fQ0FDc3d318GeNaKyNiFC5cmX+/PPPVOPVmqaUqDkkhUKhsBAuLi76upvy5csTGhqqK6TY2FjOnj1rTfGyPUohKRQKhYXo3bs3AwYMwMfHB6PRyMqVKxk1ahTVq1fHx8eHAwcOWFvEbI2WHReWJT4xVh0/YTlUnaRE1UlKsmudJDfZZSURERGAMrMl5nnrJPn/LfHxE+rEWIVCoVBkS5RCUigUCkW2QCkkhUKhUGQLlEJSKBQKRbZAKSSFQqFQZAuUQlIoFApFtkApJIVCoXhONE2jR48e+ve4uDjc3d1p1aqVFaXKOq5evcovv/ySafkrhaRQKHId169fZ8yYMfj5+fHGG2+wdOlSjEZjhvN1cnLizJkzREZGArB9+3aKFy+e4XzTgzWOrVAKSaFQKNLAqVOnqFatGl9++SW7d+9m/fr1dOvWjc6dO1tEKbVo0YJNmzYBsHTpUrp27arHRURE0LdvX+rUqUONGjVYt24dEN+QN2zYkJo1a1KzZk19x4YbN27QqFEjfHx8qFKlCnv37gXA2dlZz3PlypX07t0biN8JYvjw4fj5+TFq1Kgnlvfzzz/Ttm1bWrduTenSpZkxYwbfffcdNWrUwNfXl3v37gHwzz//0Lx5c2rVqkXDhg05f/68Xs7QoUNp0KABL730EitXrgRg9OjR7N27Fx8fH2bMmMFff/1F3bp18fHxoVq1aly6dCljlSsi2e4KCwsT85VeAgICJCAgIN3350ZUnaRE1UlKsmud3L59+7nS+fr6CiB+fn6yYcMGmTFjhuTLl08AWbRoUbrKfvTokTx69EicnJzk5MmT0r59e4mMjJTq1avLrl27pGXLliIi8tFHH+ll3L9/X7y9veXRo0cSEREhkZGRIiJy8eJFqVWrloiITJ48WSZMmCAiInFxcfLgwQMREXFyctLLXrFihfTq1UtERHr16iUtW7aUuLi4p5Y3f/58KVOmjDx48EBu374trq6u4u/vLyIiw4YNkylTpoiISJMmTeTixYsiInLw4EHx8/PTy+nQoYMYjUY5e/aslClTRkQkyW999OiRvPPOO7J48WIREYmOjpbHjx+nqLvk/7dk7XuStl/t9q1QKHINly9f5uDBg7i6urJ+/Xp9pGFjY8O7777LL7/8wltvvZWhMqpVq8bVq1dZunQpr7/+epK43377jfXr1+tHmkdFRREUFESxYsUYPHgwJ06cwMbGRj8Bt06dOvTt25fY2Fjatm2Lj4/PM8vv2LEjNjY2Ty0PwM/PDxcXF1xcXMiXLx+tW7cGoGrVqpw6dYpHjx5x4MABOnbsqOcdHR2tf27bti0Gg4FKlSpx69atVGWpV68eX331FcHBwbRr1w5vb+/nqsMnkWGFpGmaA7AHyJOQ30oRGadpWmngV6AgcAzoISIxmqblARYCtYC7QGcRuZpRORQKhcK8T5qHh0cSs1fZsmWB+EPxLEGbNm0YOXIku3fv5u7du3q4iLBq1SrKly+fJP1nn31GkSJFOHnyJCaTCQcHBwAaNWrEnj172LRpEz169OCDDz6gZ8+eSY5mj4qKSpJX4r3jnlTeoUOHyJMnj/7dYDDo3w0GA3FxcZhMJvLnz8+JEydS/Y2J75cn7HnaqVMnGjVqxKZNm3jttdf46aefaNKkSappnwdLzCFFA01EpDrgAzTXNM0XmARMERFv4D7QLyF9P+C+iJQFpiSkUygUigxTvnx58uXLx8WLF1mzZg0Q36BPmTIFAF9fX4uU07dvXz799FOqVq2aJPy1115j+vTpegN+/PhxIF5RFi1aFIPBwKJFi/S5rMDAQAoXLkz//v3p168fx44dA6BIkSKcO3cOk8mk/47UeFJ5z4OrqyulS5dmxYoVQLzSOXny5FPvSXy8BsCVK1d46aWXGDp0KG3atOHUqVPPXX5qZFghmU2sCV/tEi4BmgArE8IXAG0TPr+R8J2E+P9qibsDCoVCkU4cHR0ZMWIEAO3ataNKlSp4enqyefNmXF1dGTp0qEXK8fT05L333ksRPnbsWGJjY6lWrRpVqlRh7NixAAwcOJAFCxbg6+vLxYsX9VHO7t278fHxoUaNGqxatUrPc+LEibRq1YomTZokOWX2ect7XpYsWcLcuXOpXr06lStX1p0inkS1atWwtbWlevXqzJgxg1WrVlGlShV8fHw4f/48PXv2TFP5ybHI8ROaptkAR4GywA/AN8DBhFEQmqaVALaISBVN084AzUUkOCHuH6CeiNwx55f4+IkMe20oFIpcgbOzMyVKlHhmOpPJxMSJE5k+fbrem69atSrTp0/PdkdqvAhcu3aNR48e6d8TzzMlP37CIk4NImIEfDRNyw+sASqmlizhb2qjoex3KJNCociRGAwGPv74Y4YOHcq5c+dwcXGhfPnyKENM9seiXnYiEqZp2m7AF8ivaZqtiMQBnkBIQrJgoAQQrGmaLZAPuPekPNPbo8muh4xZE1UnKVF1kpLsWiehoaFpOiDPycmJIkWKWKRsdUBfSp63TgoVKkSFChX074kP6EtOhueQNE1zTxgZoWlaXuBV4BywC+iQkKwXYDZOrk/4TkL8TrGE3VChUCgUORpLjJCKAgsS5pEMwHIR2ahp2l/Ar5qmTQCOA3MT0s8FFmma9jfxI6MuFpBBoVAoFDmcDCskETkF1Egl/DJQN5XwKKBj8nCFQqF4GgaDgaioKH0NjyL7ExUVhcHw/IY4tVODQqHIERQsWJB79+4lWQeTVZgXvxYqVCjLy86uPE+dGAwGChYs+Nx5KoWkUChyBJqmWU0hBAYGAiSZnH/RyYw6Ubt9KxQKhSJboBSSQqFQKLIFSiEpFAqFIlugFJJCoVAosgVKISkUCoUiW6AUkkKhUCiyBUohKRQKhSJboBSSQqFQKLIFSiEpFAqFIlugFJJCoVAosgVKISkUCoUiW5Dr9rK7ffs23377LStWrMBkMtG2bVtGjhyJp6entUVTKBQKxVPIVQrp5s2b1K9fn6tXr+phU6dO5ddff+XAgQO89NJL1hNOoVAoFE8lV5nsvvjiC65evUrNmjWZNWsWc+fOpWHDhty6dYuPP/7Y2uIpFAqF4inkqhHSqlWrAJg7dy5xcXEALFmyhJIlS7JmzRpMJlOaDotSKBQKRdaRq1rnqKgogCQHQuXPnx+AmJgYjEajVeRS5Hxu377NyZMnCQsLs7YoCkWuJVcpJD8/PwA+/vhjIiMjiY6OZvTo0QA0atQIOzs7a4qnyIGEhobSoUMHihYtio+PD0WKFKF///5ERERYWzSFIteRq0x2Y8eOZevWrSxZsoSVK1diMBiIjIzExsaGzz//3NriKXIYcXFxNG/enGPHjmFnZ0e5cuW4cOECP/30E7dv32bdunXWFlGhyFXkqhFSzZo1+f3336lfvz7R0dFERkZSo0YNNm/ezCuvvGJt8RQ5jI0bN3Ls2DFKlCjB33//zblz5zh58iTOzs6sX7+eEydOWFtEhSJXkasUEsB//vMfDhw4wPbt29m2bRtHjx6lWbNm1hZLkQM5cOAAAH369KFkyZIAVK1alXbt2iWJVygUliHXKSQz+fPnp2DBgmiaZm1RcgUxMTFMnjyZqlWr4uHhQYsLine truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70bce0208>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1783,9 +1764,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70bc0dc50>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1846,9 +1827,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70bb94128>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1897,9 +1878,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70bc067f0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1928,9 +1909,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70bdea908>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1959,9 +1940,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70ce52a90>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2005,9 +1986,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70cf94898>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2034,9 +2015,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a70ce80128>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
+46
-46
@@ -357,9 +357,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1db568f9048>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -483,9 +483,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1db568ddc50>"
|
||||
"<Figure size 576x144 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -612,9 +612,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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\n",
|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1db56cdec50>"
|
||||
"<Figure size 576x144 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -750,9 +750,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1db5493f9b0>"
|
||||
"<Figure size 576x144 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1370,7 +1370,7 @@
|
||||
" // Register the callback with on_msg.\n",
|
||||
" comm.on_msg(function(msg) {\n",
|
||||
" //console.log('receiving', msg['content']['data'], msg);\n",
|
||||
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
|
||||
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
|
||||
" ws.onmessage(msg['content']['data'])\n",
|
||||
" });\n",
|
||||
" return ws;\n",
|
||||
@@ -1576,7 +1576,7 @@
|
||||
{
|
||||
"data": {
|
||||
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||||
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src=\"data:image/png;base64,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 truncated
|
||||
"<div id='0c266187-3a52-4a8f-bdad-6e1a04001cdd'></div>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
@@ -1661,9 +1661,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1db576f4780>"
|
||||
"<Figure size 576x144 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1712,9 +1712,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1db576ea128>"
|
||||
"<Figure size 576x144 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2321,7 +2321,7 @@
|
||||
" // Register the callback with on_msg.\n",
|
||||
" comm.on_msg(function(msg) {\n",
|
||||
" //console.log('receiving', msg['content']['data'], msg);\n",
|
||||
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
|
||||
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
|
||||
" ws.onmessage(msg['content']['data'])\n",
|
||||
" });\n",
|
||||
" return ws;\n",
|
||||
@@ -2527,7 +2527,7 @@
|
||||
{
|
||||
"data": {
|
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"text/html": [
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truncated
|
||||
"<div id='107d6707-025f-4601-b5a1-d990ebaa9187'></div>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
@@ -2645,9 +2645,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1db57707c50>"
|
||||
"<Figure size 576x144 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2681,9 +2681,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1db576ea3c8>"
|
||||
"<Figure size 576x144 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2738,9 +2738,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1db56d24e10>"
|
||||
"<Figure size 576x144 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2771,9 +2771,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1db58889390>"
|
||||
"<Figure size 576x144 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2800,9 +2800,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1db58b2b6d8>"
|
||||
"<Figure size 576x144 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2829,9 +2829,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1db58ad2240>"
|
||||
"<Figure size 576x144 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2861,9 +2861,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1db57752b00>"
|
||||
"<Figure size 576x144 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2902,9 +2902,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1db58b25eb8>"
|
||||
"<Figure size 288x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3536,7 +3536,7 @@
|
||||
" // Register the callback with on_msg.\n",
|
||||
" comm.on_msg(function(msg) {\n",
|
||||
" //console.log('receiving', msg['content']['data'], msg);\n",
|
||||
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
|
||||
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
|
||||
" ws.onmessage(msg['content']['data'])\n",
|
||||
" });\n",
|
||||
" return ws;\n",
|
||||
@@ -3742,7 +3742,7 @@
|
||||
{
|
||||
"data": {
|
||||
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|
||||
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src=\"data:image/png;base64,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 truncated
|
||||
"<div id='7ddacf55-e4a3-459f-b3e3-b6dda67e8e6a'></div>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
@@ -4391,7 +4391,7 @@
|
||||
" // Register the callback with on_msg.\n",
|
||||
" comm.on_msg(function(msg) {\n",
|
||||
" //console.log('receiving', msg['content']['data'], msg);\n",
|
||||
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
|
||||
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
|
||||
" ws.onmessage(msg['content']['data'])\n",
|
||||
" });\n",
|
||||
" return ws;\n",
|
||||
@@ -4597,7 +4597,7 @@
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<img src=\"data:image/png;base64,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 truncated
|
||||
"<div id='bfffd05b-5df5-4ba1-abcd-3141e2dab6ac'></div>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
@@ -5214,7 +5214,7 @@
|
||||
" // Register the callback with on_msg.\n",
|
||||
" comm.on_msg(function(msg) {\n",
|
||||
" //console.log('receiving', msg['content']['data'], msg);\n",
|
||||
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
|
||||
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
|
||||
" ws.onmessage(msg['content']['data'])\n",
|
||||
" });\n",
|
||||
" return ws;\n",
|
||||
@@ -5420,7 +5420,7 @@
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<img src=\"data:image/png;base64,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 truncated
|
||||
"<div id='b0e80102-eb41-4c93-905f-0105a8181b98'></div>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
@@ -5456,9 +5456,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1db588608d0>"
|
||||
"<Figure size 576x396 with 6 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -5657,9 +5657,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1db5768b400>"
|
||||
"<Figure size 576x144 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -5706,9 +5706,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1db58b4f9b0>"
|
||||
"<Figure size 576x144 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -5739,9 +5739,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1db58b1bac8>"
|
||||
"<Figure size 576x396 with 4 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
+67
-71
@@ -16,7 +16,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -255,7 +255,7 @@
|
||||
"<IPython.core.display.HTML object>"
|
||||
]
|
||||
},
|
||||
"execution_count": 36,
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -373,7 +373,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
@@ -405,7 +405,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 38,
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -462,7 +462,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -480,7 +480,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -531,7 +531,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 41,
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -570,7 +570,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -600,16 +600,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 43,
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -635,14 +635,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 44,
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -652,8 +652,8 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"mean = 1.783\n",
|
||||
"std = 0.143\n"
|
||||
"mean = 1.797\n",
|
||||
"std = 0.146\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -689,7 +689,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 45,
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -722,7 +722,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -759,14 +759,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 47,
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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\n",
|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -800,7 +800,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 48,
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -838,14 +838,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 49,
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -877,14 +877,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -915,14 +915,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -979,16 +979,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 52,
|
||||
"execution_count": 17,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1030,7 +1030,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 53,
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -1076,7 +1076,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 54,
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -1102,16 +1102,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"execution_count": 20,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1157,14 +1157,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1187,18 +1187,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 57,
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "eeeb2de72db747209765963d4fd91912",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"interactive(children=(FloatSlider(value=5.0, description='mu', max=10.0), FloatSlider(value=0.6000000000000001…"
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1384,7 +1380,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 58,
|
||||
"execution_count": 23,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
@@ -1414,7 +1410,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 59,
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -1443,16 +1439,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 60,
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[ 6.617 5.3 3.104 5.279 7.17 3.813 -0.221\n",
|
||||
" 1.413 -0.555 -2.062 0.492 -1.039 5.578 -3.175\n",
|
||||
" 0.609]\n"
|
||||
"[ 3.532 2.938 5.114 3.104 5.776 3.19 -1.491\n",
|
||||
" 4.938 1.16 4.501 1.997 2.38 4.186 6.011\n",
|
||||
" 1.8 ]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1470,7 +1466,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 61,
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -1495,7 +1491,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 62,
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -1533,12 +1529,12 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 63,
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
]
|
||||
@@ -1568,7 +1564,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 64,
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -1586,12 +1582,12 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 65,
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
]
|
||||
@@ -1616,7 +1612,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 66,
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -1635,12 +1631,12 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 67,
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
]
|
||||
@@ -1678,16 +1674,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 68,
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"DescribeResult(nobs=5000, minmax=(-3.3558882657362066, 24.02783608230256), mean=10.036498244806243, variance=2.96816439267424, skewness=-0.035679621633594914, kurtosis=3.8111268433571883)"
|
||||
"DescribeResult(nobs=5000, minmax=(-0.3759394593501959, 21.349502007521362), mean=10.002526325264105, variance=2.8118142566927693, skewness=-0.05493420847181719, kurtosis=2.305266649637539)"
|
||||
]
|
||||
},
|
||||
"execution_count": 68,
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1706,16 +1702,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 69,
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"DescribeResult(nobs=10, minmax=(-0.04693943665506825, 0.6562007196788996), mean=0.34537366184107265, variance=0.0613118821936125, skewness=-0.21435079986652267, kurtosis=-1.2763449406970233)\n",
|
||||
"DescribeResult(nobs=10, minmax=(-2.2922970571743235, 2.07937157289957), mean=-0.39398879831470823, variance=1.6367898886878445, skewness=0.4089745645544576, kurtosis=-0.44193847413985043)\n",
|
||||
"\n",
|
||||
"DescribeResult(nobs=300000, minmax=(-4.742809555723424, 4.672447921701554), mean=0.002356925171581094, variance=0.9970954717330218, skewness=0.007232863756995517, kurtosis=-0.0030043672414206135)\n"
|
||||
"DescribeResult(nobs=300000, minmax=(-4.8659578698172155, 4.692624465524303), mean=0.00014668102226286805, variance=1.001370952607716, skewness=0.0011541152522754141, kurtosis=-0.0006976512418868097)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
@@ -312,9 +312,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a7fa11e4e0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -345,14 +345,14 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Mean of readings is 10.030\n"
|
||||
"Mean of readings is 9.933\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a7faecab70>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -408,9 +408,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a7f91b4278>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -725,9 +725,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a7faf1ad30>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -769,9 +769,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a7fc062c88>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -822,9 +822,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a7fc0f0550>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -832,9 +832,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a7fc1562b0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -876,28 +876,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "cd690b1598f6483ca002eb2f526c176f",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/html": [
|
||||
"<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
|
||||
"<p>\n",
|
||||
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
|
||||
" that the widgets JavaScript is still loading. If this message persists, it\n",
|
||||
" likely means that the widgets JavaScript library is either not installed or\n",
|
||||
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
|
||||
" Widgets Documentation</a> for setup instructions.\n",
|
||||
"</p>\n",
|
||||
"<p>\n",
|
||||
" If you're reading this message in another frontend (for example, a static\n",
|
||||
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
|
||||
" it may mean that your frontend doesn't currently support widgets.\n",
|
||||
"</p>\n"
|
||||
],
|
||||
"image/png": 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|
||||
"text/plain": [
|
||||
"interactive(children=(FloatSlider(value=7.5, description='m1', max=10.0, min=5.0, step=0.5), FloatSlider(value=12.0, description='m2', max=15.0, min=10.0, step=0.5), FloatSlider(value=1.0, description='v1', max=2.0, min=0.1), FloatSlider(value=1.0, description='v2', max=2.0, min=0.1), Output()), _dom_classes=('widget-interact',))"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1616,7 +1597,7 @@
|
||||
" // Register the callback with on_msg.\n",
|
||||
" comm.on_msg(function(msg) {\n",
|
||||
" //console.log('receiving', msg['content']['data'], msg);\n",
|
||||
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
|
||||
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
|
||||
" ws.onmessage(msg['content']['data'])\n",
|
||||
" });\n",
|
||||
" return ws;\n",
|
||||
@@ -1822,7 +1803,7 @@
|
||||
{
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"data": {
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truncated
|
||||
"<div id='20074e2c-c4ce-492d-83d7-54ddf8145182'></div>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
@@ -2000,9 +1981,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a7fc1aa710>"
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2151,9 +2132,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a7fc108198>"
|
||||
"<Figure size 792x216 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2301,9 +2282,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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\n",
|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a7fc005f98>"
|
||||
"<Figure size 576x144 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2334,9 +2315,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a7faf47b00>"
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2373,9 +2354,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a7fc28e5f8>"
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2448,9 +2429,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a7fc133630>"
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2465,9 +2446,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a7fc5fedd8>"
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2565,9 +2546,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a7fc096470>"
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2621,9 +2602,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a7faf2bf60>"
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2676,9 +2657,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a7faf7c128>"
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2728,9 +2709,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a7fae7e4a8>"
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2774,9 +2755,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a7fc12c5c0>"
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2828,28 +2809,8 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "2b03522925d14d4abeb8020d3557f942",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/html": [
|
||||
"<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
|
||||
"<p>\n",
|
||||
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
|
||||
" that the widgets JavaScript is still loading. If this message persists, it\n",
|
||||
" likely means that the widgets JavaScript library is either not installed or\n",
|
||||
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
|
||||
" Widgets Documentation</a> for setup instructions.\n",
|
||||
"</p>\n",
|
||||
"<p>\n",
|
||||
" If you're reading this message in another frontend (for example, a static\n",
|
||||
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
|
||||
" it may mean that your frontend doesn't currently support widgets.\n",
|
||||
"</p>\n"
|
||||
],
|
||||
"text/plain": [
|
||||
"interactive(children=(IntSlider(value=0, description='start_pos', max=10, min=-10), FloatSlider(value=5.0, continuous_update=False, description='sensor_noise'), FloatSlider(value=1.0, continuous_update=False, description='velocity', max=2.0, min=-2.0), FloatSlider(value=5.0, continuous_update=False, description='process_noise'), Output()), _dom_classes=('widget-interact',))"
|
||||
"<Figure size 576x216 with 0 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2891,28 +2852,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "74187f4a66f94eb6a9b3df68ed2bcc50",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/html": [
|
||||
"<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
|
||||
"<p>\n",
|
||||
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
|
||||
" that the widgets JavaScript is still loading. If this message persists, it\n",
|
||||
" likely means that the widgets JavaScript library is either not installed or\n",
|
||||
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
|
||||
" Widgets Documentation</a> for setup instructions.\n",
|
||||
"</p>\n",
|
||||
"<p>\n",
|
||||
" If you're reading this message in another frontend (for example, a static\n",
|
||||
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
|
||||
" it may mean that your frontend doesn't currently support widgets.\n",
|
||||
"</p>\n"
|
||||
],
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"interactive(children=(IntSlider(value=0, description='start_pos', max=10, min=-10), FloatSlider(value=5.0, continuous_update=False, description='sensor_noise'), FloatSlider(value=1.0, continuous_update=False, description='velocity', max=2.0, min=-2.0), FloatSlider(value=0.1, continuous_update=False, description='process_noise', max=40.0), Output()), _dom_classes=('widget-interact',))"
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3002,9 +2944,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a7fc188ac8>"
|
||||
"<Figure size 576x216 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3145,7 +3087,7 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(11.364, 4.455)"
|
||||
"(11.363636363636363, 4.454545454545454)"
|
||||
]
|
||||
},
|
||||
"execution_count": 35,
|
||||
|
||||
@@ -335,9 +335,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAaoAAAEDCAYAAABknEDQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3XucjeX+//HXR05NMqJEmbYcUlGk+VLSVkpntNNhyuzdFvmmE5WQhhkj9jjmbJNIQg7lsKlvZHfachrHSIyo4SfHYZFpaLh+f6zFHpo1hpk1a62Z9/PxmMfc93WfPpeZez6u+77WdZlzDhERkVBVLNgBiIiI5ESJSkREQpoSlYiIhDQlKhERCWlKVCIiEtKKBzuAc+HxeNRFUUSkkIuMjLSs62pRiYhISFOiEhGRkBZWj/6yioyMPK/jkpOTAYiOjs7PcApUuNch3OMH1SFUqA7Bl1/xezwev9vUohIRkZCmRCUiIiFNiUpEREKaEpWIiIS0sO1MISIiwbV9fzpDl3lIyzhOjS2r6dysFlEVIvL9OkpUIiJyzrbvT6f1u8tITcsAYMPenaxOPcjktg3zPVnp0Z+IiJyzgQs3kZqWflpZalo6AxduyvdrKVGJiMg5230oI9vyPX7K80KJSkREztnlZUtnW17RT3leKFGJiMg569ysFleVP/1d1FXlI+jcrFa+X0uJSkREzllUhQgmt23I7VGlqXNZCVrWuyIgHSlAvf5EROQ8RVWIoGND77ir0dE3Bew6alGJiEhIU6ISEZGQpkQlIiIhTYlKRERCmhKViIiENCUqEREJaUpUIiIS0pSoREQkpAUsUZlZlJl9YWYbzWyDmXX0lZc3s4VmluL7fomv3MxsmJltMbN1ZlY/ULGJiEj+2LRpE5mZmQG9RiBbVJnAa86564BbgBfM7HqgG7DIOVcTWORbB7gfqOn7ag+MDmBsIiKSB9999x2PPPIIsbGxfPLJJwG9VsASlXPuF+fcKt/yYWAjcCXQEpjo220i8LBvuSXwvvNaCpQzs8qBik9ERM7d5s2beeqpp6hbty6LFi2iffv2NG3aNKDXNOdcQC8AYGZVga+BOkCqc65clm0HnHOXmNk8IMk59x9f+SKgq3Mu+eS+Ho/nVLApKSkBj1tERLx27tzJu+++y/z58ylRogQxMTHExsYSGRmZL+evWbPmqeXIyEjLui3gg9KaWRngI6CTc+6QmfndNZuywGdRERHxa+/evYwfP57Zs2dTrFgxnnjiCZ5++mnKly9fYDEENFGZWQm8SWqyc+5jX/FuM6vsnPvF92hvj698BxCV5fAqwE5/546Ojj6vmJKTk/N0fCgI9zqEe/ygOoQK1SFw9uzZQ79+/Rg1ahSZmZm0a9eON998kypVqpy2X37F7/F4/G4LZK8/A94FNjrnBmfZNBd42rf8NDAnS/nffL3/bgE8zrlfAhWfiIj80YEDB3jzzTepVq0aQ4YMISYmhs2bNzN69Og/JKmCEsgW1W3AX4HvzGyNr6w7kARMN7O2QCrwmG/bJ8ADwBYgHWgTwNhERCSLw4cPM3ToUAYOHIjH4yEmJoaEhARq1cr/GXvPVcASla9ThL8XUndls78DXghUPCIi8kfp6emMGjWKpKQk9u/fT8uWLUlMTOTGG28MdminaGQKEZEi6OjRo4wcOZIaNWrw+uuvEx0dzfLly5k9e3ZIJSnQVPQiIkVKZmYmEydOJDExkdTUVP785z8zbdo0br/99mCH5pdaVCIiRcDx48eZMmUK1113He3ataNSpUosWLCAL7/8MqSTFChRiYgUas45Pv74Y+rWrUvr1q256KKLmDt3LkuXLqVZs2bk8NnWkKFEJSJSCDnn+OSTT4iOjqZVq1ZkZmYybdo0Vq1aRfPmzcMiQZ2kRCUiUsh88cUXNG7cmAcffJADBw4wceJE1q9fz+OPP06xYuH3Zz/8IhYRkWwtWbKEu+66i6ZNm5KamsqYMWPYtGkTf/vb3yhePHz7zilRiYiEuVWrVvHggw/SqFEj1q9fz5AhQ0hJSaF9+/aUKFEi2OHlmRKViEiY2rBhA48++ig333wzS5YsISkpia1bt9KxY0dKly4d7PDyTfi2BUVEiqgtW7aQkJDAlClTKFOmDPHx8bzyyiv5NuVGqFGiEhEJE6mpqfTu3ZsJEyZQsmRJunTpwuuvv06FChWCHVpAKVGJiIS4X375hb59+zJ27FgAXnzxRbp160alSpWCHFnBUKISEQlR+/bto1+/fowYMYLMzEyeeeYZ4uLiiIqKOvvBhYgSlYhIiDl48CCDBg1iyJAhpKenExsbS8+ePalevXqwQwsKJSoRkRDx66+/MmzYMAYMGMDBgwd5/PHHSUhI4Lrrrgt2aEGlRCUiEmQZGRkMHjyYpKQk9u7dS/PmzUlMTKRevXrBDi0kKFGJiATJsWPHmDlzJuPHj2fv3r00a9aM3r1707Bhw2CHFlKUqEREClhmZiaTJk2iV69e/Pzzz9SrV48ZM2bQpEmTYIcWkjQyhYhIATlx4gQffvghtWvX5plnnuGyyy5j6NChjB07VkkqB0pUIiIB5pxj9uzZ1K1blyeffJJSpUoxe/Zsli9fTqNGjcJqyo1gUKISEQkQ5xz/93//R4MGDfjLX/7CsWPHmDp1KmvWrKFly5ZKULmkRCUiEgAnp3i///772bdvHxMmTGDDhg3ExMSE5ZxQwaR/LRGRfHRyivc777yTbdu2MWrUKDZt2sTf//73sJ4TKpiUqERE8sGaNWto3rw5t956K2vXrmXw4MFs2bKFDh06ULJkyWCHF9aU3kVE8mDjxo3Ex8czY8YMypUrR9++fXnppZcoU6ZMsEMrNJSoRETOw48//kivXr2YPHkyERER9OjRg1dffZVy5coFO7RCJ2CP/sxsvJntMbP1WcrqmdlSM1tjZslm1sBXbmY2zMy2mNk6M6sfqLhERPIiNTWV9u3bU6tWLWbOnMlrr73Gtm3bSExMPOcktX1/OkOXeYj/Ko2OH65m+/70AEUd3gLZonoPGAG8n6WsP9DLOfepmT3gW78DuB+o6ftqCIz2fRcRCQm7du2ib9++jBkzBuccHTp0oHv37lSuXPm8zrd9fzqt311GaloGABv27mR16kEmt21IVIWI/Aw97JlzLnAnN6sKzHPO1fGtfwaMd85NM7MngebOuafMbAzwpXNuqm+/TcAdzrlfsp7P4/GcCjYlJSVgcYuInHTw4EEmTZrEtGnTyMzM5KGHHqJt27bnnaBOGrrMwzfbM/5QfntUaTo2LJxTyuekZs2ap5YjIyNP+4BZQb+j6gR8ZmYD8T52bOQrvxLYnmW/Hb6yXxARCYJff/2VyZMnM3XqVNLT07n33ntp3759vk1amJZxPNvyA37Ki7KCTlQdgFeccx+Z2ePAu8DdQHYfz86xqRcdHX1eASQnJ+fp+FAQ7nUI9/hBdQgVgajDkSNHGD58OP379+fAgQO0atWKXr16Ubt27Xy7BkCNLavZsHfnH8qrX3kZ0dE35eu1Aim/fgYej8fvtoL+HNXTwMe+5RlAA9/yDiDrf1OqAH/8CYqIBEhGRgZDhgyhWrVqvPHGGzRq1IiVK1cyc+bMfE9SAJ2b1eKq8qe/i7qqfASdm9XK92uFu4JOVDuBk0MENwVOvmiaC/zN1/vvFsBz5vspEZFAOHbsGP/85z+pUaMGr7zyCnXq1GHx4sXMmzeP+vUD1wE5qkIEk9s25Pao0tS5rAQt612hjhR+BOzRn5lNxduj71Iz2wHEA88CQ82sOJABtPft/gnwALAFSAfaBCouERHwzgk1efJkevXqxbZt27j11lt5//33adq0aYHFEFUh4lTHiXB63FfQApaonHNP+tl0czb7OuCFQMUiInLSiRMnmDFjBvHx8WzatIn69eszcuRI7rvvPo1mHqI01p+IFAnOOebOnctNN91ETEwMF1xwAR999BHJycncf//9SlIhTIlKRAo15xwLFiygYcOGtGzZkvT0dCZPnsy6det45JFHlKDCgBKViBRaX3/9NU2aNOHee+9l9+7djBs3ju+//56nnnqKCy64INjhSS5pUFoRyXfb96czcOEmdh/K4PKypencrFaB9mZbvnw5PXr0YMGCBVSqVIkRI0bQrl07SpUqVWAxSP5RohKRfPXfMez+O8BqQY1ht3btWnr27MncuXOpUKECAwYM4PnnnyciQl2+w5ke/YlIvhq4cNNpSQogNc3bwgqUH374gSeeeIJ69erx1Vdf0bt3b7Zt20bnzp2VpAoBtahEJF/tPvTHgVYB9vgpz4sdO3Ywbtw4Pv30Uy688EK6d+9O586dueSSS/L9WhI8SlQikq8uL1s62/KKfsrPx44dO+gWl8CUSe9hxYpR976neHdQb266tmq+XUNChx79iUi+CuQYdrt376ZTp07UqFGDKR9M5KK691G5/Tuk3RBDx7nbNPFgIaUWlYjkq5Nj2A1cuIk9hzKomA+9/tLS0hgwYADDhg0jIyODa29/CM91LSkeefmpfU6+Bxsao6GIChslKhHJd1EVIvIlYRw6dIghQ4YwaNAgDh8+TExMDAkJCfT8cj9Lt6b9Yf9AvAeT4NOjPxEJOUeOHKF///5cffXVxMfH07RpU9auXcuUKVO45pprCuQ9mIQOJSoRCRlHjx5l2LBhVK9ena5du9KgQQNWrFjBrFmzuOGGG07tp7mcipYcH/2Z2a1ALHA7UBn4DVgPzAc+cM75n5JRRCSXfv/9d9577z169+7N9u3badKkCTNnzqRx48bZ7n/yPVj3D5dwIOM41a+8rMBHv5CC4zdRmdmneCc6nAP0AfYApYFrgDuBOWY22Dk3tyACFZHC5/jx40yZMoWEhAS2bt1Kw4YNmTBhAk2bNj3rYLGay6noyKlF9Vfn3L4zyn4FVvm+BpnZpQGLTEQKrRMnTvDxxx/Ts2dPNm7cSL1Line truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17681eabb70>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -563,9 +563,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17682c42d30>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -603,9 +603,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17682c89320>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -615,8 +615,8 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[10.6 2.923]\n",
|
||||
" [ 2.923 2.365]]\n"
|
||||
"[[10.665 2.986]\n",
|
||||
" [ 2.986 2.406]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -644,9 +644,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17682caf208>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -656,8 +656,8 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[0.994 0.006]\n",
|
||||
" [0.006 1. ]]\n"
|
||||
"[[ 1. -0.002]\n",
|
||||
" [-0.002 1.006]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -706,9 +706,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17682caf470>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -886,9 +886,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17684c90f28>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -926,9 +926,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17684d48860>"
|
||||
"<Figure size 432x288 with 3 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -968,9 +968,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x176846cc278>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1004,9 +1004,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17684bedb70>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1037,9 +1037,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17682c28d68>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1080,9 +1080,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17682c84278>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1123,9 +1123,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x176846da550>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1179,9 +1179,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17684b9b710>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1210,9 +1210,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17684c66be0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1245,28 +1245,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "572eb91274114c8b956ddfa873a0b2f7",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/html": [
|
||||
"<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
|
||||
"<p>\n",
|
||||
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
|
||||
" that the widgets JavaScript is still loading. If this message persists, it\n",
|
||||
" likely means that the widgets JavaScript library is either not installed or\n",
|
||||
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
|
||||
" Widgets Documentation</a> for setup instructions.\n",
|
||||
"</p>\n",
|
||||
"<p>\n",
|
||||
" If you're reading this message in another frontend (for example, a static\n",
|
||||
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
|
||||
" it may mean that your frontend doesn't currently support widgets.\n",
|
||||
"</p>\n"
|
||||
],
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"interactive(children=(FloatSlider(value=5.0, continuous_update=False, description='var_x', max=20.0), FloatSlider(value=5.0, continuous_update=False, description='var_y', max=20.0), FloatSlider(value=1.5, continuous_update=False, description='cov_xy', max=50.0, step=0.2), Output()), _dom_classes=('widget-interact',))"
|
||||
"<Figure size 288x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1326,7 +1307,7 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0.9540"
|
||||
"0.9539731096080193"
|
||||
]
|
||||
},
|
||||
"execution_count": 27,
|
||||
@@ -1354,7 +1335,7 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"-0.9178"
|
||||
"-0.9178223453527256"
|
||||
]
|
||||
},
|
||||
"execution_count": 28,
|
||||
@@ -1382,9 +1363,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17684d07588>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1413,9 +1394,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17684da3dd8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1456,9 +1437,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1768456db00>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1515,9 +1496,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17684d0d400>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1545,9 +1526,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17684d0d390>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1578,9 +1559,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17682c3ef28>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1614,9 +1595,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17682ca7da0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1643,9 +1624,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17684bea940>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1675,9 +1656,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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\n",
|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x176845a2ef0>"
|
||||
"<Figure size 576x144 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1706,9 +1687,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17684781b00>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1737,9 +1718,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17684bc3048>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1770,9 +1751,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x176845d2b38>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1809,9 +1790,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17681eab828>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1836,9 +1817,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x176fd038c18>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1870,9 +1851,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x176845c4860>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1897,9 +1878,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1768286c358>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1926,9 +1907,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17684d905c0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1955,9 +1936,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x176ffe221d0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2036,9 +2017,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17684804940>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
@@ -362,9 +362,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f336b32e8>"
|
||||
"<Figure size 792x216 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1014,9 +1014,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f363a89b0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1482,8 +1482,11 @@
|
||||
"P = [[500. 0.]\n",
|
||||
" [ 0. 500.]]\n",
|
||||
"x_prior = [[0. 0.]].T\n",
|
||||
"P_prior = [[500. 0.]\n",
|
||||
" [ 0. 500.]]\n",
|
||||
"P_prior = [[1. 0.]\n",
|
||||
" [0. 1.]]\n",
|
||||
"x_post = [[0. 0.]].T\n",
|
||||
"P_post = [[1. 0.]\n",
|
||||
" [0. 1.]]\n",
|
||||
"F = [[1. 0.1]\n",
|
||||
" [0. 1. ]]\n",
|
||||
"Q = [[0. 0. ]\n",
|
||||
@@ -1493,12 +1496,15 @@
|
||||
"K = [[0. 0.]].T\n",
|
||||
"y = [[0.]]\n",
|
||||
"S = [[0.]]\n",
|
||||
"SI = [[0.]]\n",
|
||||
"M = [[0.]]\n",
|
||||
"B = None\n",
|
||||
"z = [[0.]]\n",
|
||||
"z = [[None]]\n",
|
||||
"log-likelihood = -708.3964185322641\n",
|
||||
"likelihood = 2.2250738585072014e-308\n",
|
||||
"mahalanobis = 0.0\n",
|
||||
"alpha = 1.0\n",
|
||||
"inv = <function inv at 0x0000021F338E4B70>"
|
||||
"inv = <function inv at 0x0000027DC95E1D90>"
|
||||
]
|
||||
},
|
||||
"execution_count": 27,
|
||||
@@ -1598,9 +1604,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f363cd390>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1608,9 +1614,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f36499e80>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1648,9 +1654,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f36573f28>"
|
||||
"<Figure size 648x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1750,7 +1756,11 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"['dim_x',\n",
|
||||
"['alpha',\n",
|
||||
" 'likelihood',\n",
|
||||
" 'log_likelihood',\n",
|
||||
" 'mahalanobis',\n",
|
||||
" 'dim_x',\n",
|
||||
" 'dim_z',\n",
|
||||
" 'dim_u',\n",
|
||||
" 'x',\n",
|
||||
@@ -1766,12 +1776,15 @@
|
||||
" 'K',\n",
|
||||
" 'y',\n",
|
||||
" 'S',\n",
|
||||
" 'SI',\n",
|
||||
" '_I',\n",
|
||||
" 'x_prior',\n",
|
||||
" 'P_prior',\n",
|
||||
" 'compute_log_likelihood',\n",
|
||||
" 'log_likelihood',\n",
|
||||
" 'likelihood',\n",
|
||||
" 'x_post',\n",
|
||||
" 'P_post',\n",
|
||||
" '_log_likelihood',\n",
|
||||
" '_likelihood',\n",
|
||||
" '_mahalanobis',\n",
|
||||
" 'inv']"
|
||||
]
|
||||
},
|
||||
@@ -1802,9 +1815,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f36ed6cf8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1958,9 +1971,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f36b1a470>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2004,28 +2017,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "7c0d25f017ed4be1a9c3e0e8b59bc715",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/html": [
|
||||
"<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
|
||||
"<p>\n",
|
||||
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
|
||||
" that the widgets JavaScript is still loading. If this message persists, it\n",
|
||||
" likely means that the widgets JavaScript library is either not installed or\n",
|
||||
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
|
||||
" Widgets Documentation</a> for setup instructions.\n",
|
||||
"</p>\n",
|
||||
"<p>\n",
|
||||
" If you're reading this message in another frontend (for example, a static\n",
|
||||
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
|
||||
" it may mean that your frontend doesn't currently support widgets.\n",
|
||||
"</p>\n"
|
||||
],
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"interactive(children=(IntSlider(value=1, continuous_update=False, description='F00', max=2), FloatSlider(value=1.0, continuous_update=False, description='F01(dt)', max=2.0), FloatSlider(value=0.0, continuous_update=False, description='F10', max=2.0), FloatSlider(value=1.0, continuous_update=False, description='F11', max=2.0), FloatSlider(value=0.0, continuous_update=False, description='covar', max=1.0), Output()), _dom_classes=('widget-interact',))"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2219,9 +2213,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f3655cc50>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2349,9 +2343,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f36563fd0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2438,9 +2432,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f36413278>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2465,9 +2459,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f364e73c8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2567,9 +2561,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f363ddbe0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2577,9 +2571,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f36596dd8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2621,9 +2615,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f36a31470>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2649,9 +2643,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f36a31cf8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2678,9 +2672,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f36980a90>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2718,9 +2712,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f369c33c8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2728,9 +2722,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f349a41d0>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2771,9 +2765,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f3655cac8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2781,9 +2775,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f364e7198>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2813,9 +2807,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f36978c18>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2844,9 +2838,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f363d45c0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2877,9 +2871,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f3651c5f8>"
|
||||
"<Figure size 720x432 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2959,9 +2953,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f36650be0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3081,9 +3075,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f364d64a8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3116,9 +3110,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f36bccdd8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3157,9 +3151,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f363e3978>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3197,9 +3191,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x21f363ed7f0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3252,9 +3246,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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||||
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|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
+29
-26
@@ -239,11 +239,14 @@
|
||||
" },\n",
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||||
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
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||||
" \"HTML-CSS\": {\n",
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" scale: 95,\n",
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" },\n",
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||||
" \"fast-preview\": {\n",
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||||
" \"disabled\": false\n",
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||||
" }\n",
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||||
" });\n",
|
||||
"</script>\n"
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||||
@@ -561,8 +564,8 @@
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},
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"execution_count": 2,
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||||
@@ -1067,8 +1070,8 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" [0.5 1. ]]\n"
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]
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}
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],
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@@ -1089,9 +1092,9 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" [0.167 0.5 1. ]]\n"
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}
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@@ -1116,8 +1119,8 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[ 0.25 0.5]\n",
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" [ 0.5 1.0]]\n"
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" [0.5 1. ]]\n"
|
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]
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}
|
||||
],
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@@ -1135,9 +1138,9 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[ 0.25 0.5 0.5]\n",
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" [ 0.5 1.0 1.0]]\n"
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"[[0.25 0.5 0.5 ]\n",
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" [0.5 1. 1. ]\n",
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||||
" [0.5 1. 1. ]]\n"
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||||
]
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||||
}
|
||||
],
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||||
@@ -1164,9 +1167,9 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[ 0.00000002 0.00000078 0.00002083]\n",
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" [ 0.00000078 0.00004167 0.00125 ]\n",
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" [ 0.00002083 0.00125 0.05 ]]\n"
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"[[0.00000002 0.00000078 0.00002083]\n",
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" [0.00000078 0.00004167 0.00125 ]\n",
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" [0.00002083 0.00125 0.05 ]]\n"
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]
|
||||
}
|
||||
],
|
||||
@@ -1414,9 +1417,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x15d247b5b38>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1447,9 +1450,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x15d2729bc18>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1525,9 +1528,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x15d2742d5f8>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1554,9 +1557,9 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"exact answer= 54.5981500331\n",
|
||||
"exact answer= 54.598150033144236\n",
|
||||
"euler answer= 54.59705808834125\n",
|
||||
"difference = 0.00109194480299\n",
|
||||
"difference = 0.0010919448029866885\n",
|
||||
"iterations = 400000\n"
|
||||
]
|
||||
}
|
||||
@@ -1678,9 +1681,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x15d272a1828>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
+104
-104
@@ -342,9 +342,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed30b2780>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -634,9 +634,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed3e7cdd8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -748,9 +748,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed3e65470>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -856,9 +856,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed3f78438>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1158,9 +1158,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed3e7d160>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1208,9 +1208,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed3f00c50>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1239,9 +1239,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed3eea358>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1270,9 +1270,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed507a160>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1303,9 +1303,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed3ef6e48>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1337,9 +1337,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed3f29438>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1372,9 +1372,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed51fb358>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1409,9 +1409,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed5201f28>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1451,9 +1451,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed5128128>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1482,9 +1482,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed524f9b0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1492,9 +1492,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed524fa20>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1532,9 +1532,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
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||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed3fc4cc0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1542,9 +1542,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed3fa6080>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1582,9 +1582,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed4fbb780>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1616,9 +1616,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed5415400>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1670,9 +1670,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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||||
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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed51e64e0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1680,9 +1680,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed51e6048>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1718,9 +1718,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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||||
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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed3fcee80>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1728,9 +1728,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed5402a58>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1761,9 +1761,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed56830b8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1771,9 +1771,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed55f9358>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1905,9 +1905,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed3194208>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2077,9 +2077,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed3e65748>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2107,9 +2107,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed3f23f98>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2178,9 +2178,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed50e1d68>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2455,9 +2455,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed52c4208>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2491,9 +2491,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed505ab38>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2568,9 +2568,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed5394ba8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2665,9 +2665,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed55d0898>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2737,9 +2737,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed556d160>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2772,9 +2772,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed556d390>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2823,9 +2823,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed54770b8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2910,9 +2910,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed4fb1470>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2920,9 +2920,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed55caa20>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2930,9 +2930,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed529e208>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2987,9 +2987,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed5504780>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2997,9 +2997,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed54779e8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3007,9 +3007,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed3e99f28>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3113,9 +3113,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed5277470>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3260,9 +3260,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed548d828>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3456,9 +3456,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed55cec88>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3718,9 +3718,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed3f63080>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3840,9 +3840,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed4fb1588>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3983,9 +3983,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed56350b8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -4052,9 +4052,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
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||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed560ce80>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -4062,9 +4062,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed54b7ba8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -4124,9 +4124,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed53ecb00>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -4134,9 +4134,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1fed57e0ba8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
@@ -350,9 +350,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x229382d3da0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -402,9 +402,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x229382bd7b8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -434,9 +434,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3Xd8VGXWwPHfmcmkJxNSIKF3pCMiWAB17chasaxlV2XV9X11d98FFBDE3kDXhlt0V13WtiIoIrpix0KVTugtJJT0nkwy87x/zBADJhCSmbmT5Hw/n3wyM/fOvecmM3PmPvc8zyPGGJRSSqlQY7M6AKWUUqoumqCUUkqFJE1QSimlQpImKKWUUiFJE5RSSqmQpAlKKaVUSNIEpZRSKiRpgrKYiNwsIutFpExEDojIX0QkoYHP3S0i5/kxFr9uTymlmkITlIVEZALwJDAJcAKnAV2AxSISbmVsSillNU1QFhGReOBB4G5jzCfGmCpjzG7gGrxJ6kYReU1EHqn1nLNFZJ/v9hygM/ChiJSIyD0i0lVEjIjcLiJZIrLflwQPP/+Ethf4v4JSStUvzOoAWrEzgEhgXu0HjTElIvIxcD5QWd+TjTE3icgo4LfGmM8ARKSrb/E5QC+gO/CFiKw9vM6JbE8ppaykZ1DWSQZyjDHVdSzb71veWA8aY0qNMeuBV4FfNWFbSillCU1Q1skBkkWkrrPYNN/yxsqodXsP0L4J21JKKUtogrLOD3ib8K6s/aCIxAAXA58DpUB0rcWpR22jvqHoO9W63RnI8t1u7PaUUiroNEFZxBhTiLdI4gURuUhEHL5rSO8C+4A5wBpgjIgkikgq8MejNnMQ73Wmo00XkWgR6Q/cArzje7yx21NKqaDTBGUhY8xTwFRgFlAELMPbPHeuMaYSb5JaC+wGPuWnRHPY48A0ESkQkYm1Hv8a2I73LGyWMeZT3+ON3Z5SSgWd6ISFLYfvDGwX4Kin+EIppZoNPYNSSikVkjRBKaWUCknaxKeUUiokBXwkicLCQs2AqsVwOp1idQxKtRbaxKeUUiokaYJSSikVkoI6WKzT6Qzm7uq1cuVKAIYNG2ZxJD9pSkxdJ38EwO4nLgmZmALFipgKCwuDti+l1E/0DKoF+NXwTsdfSSmlmpmAV/HVLpLYtm1bQPfVWq0/5OLBb/KZemYCQ9MirA6nxenVq1fNbS2SUCp49AyqGXN7DAdL3cxZXwzA25tKLI5IKaX8J6jXoELlWkZLuLZSUeXm7rdWc6CwCltYJFBCTHQMrjZdqazyMLp3CsYYwuyN/w7SEv5O/qDXoJSyhs6o28wUVVTxnxUZfLAmq+ax7dk/nTk9vmgzAM8s3grAr4Z35voRnYMbpFJK+YEmqBBnjGH+6kzaRIdTXuXmL1/tOGL5uFM6sj6z/m/4by3fS6TDxmVDOmC36eUTpVTzoQkqxG05WMyr3+2uc9moXsnceFoXZizYCMDo3sms2JXPzKsH0bFNNI9+lM6K3Xm8+t1uwsNsjB3UnoIyF5EOO5EOexCPQimlTpwmqBD3xeZDdT7+wKX9OKVL4hGPTTi/D9UeQ3iY97rT+FHdWLE7D4C/fb2TH/cUsCYjnx4psTw1bhAiekallApdmqBC1LKduby9IoPth7zXlwZ0iKe9M4qxg9sTHW6nXXzkz55jswnhtZrxOiRE8eZtI7j+5WUANclq84Fi1u4rZEinhCAciVJKNY4mqBBTWe1m0fr9/HvpXlzVHgCGdk7gwcsGNGp7cZEOBndysjbjyOtUC9ZkaYJSSoU0TVAh5vXvd/Ph2v019yPCbNx+Vo8mbfNP5/dhR3YJ76zIoNzlJiO/jFV78ymqqCI+0tHUkJVSKiC0o24IcbnNEckpKTact24/jQ4JUcd83od3jTzm8sSYcE7tmsisqwcz+4ahDOmUgMdj+M+KDD5cm0WV2+OX+JVSyp/0DCpElFZ5+D6jgtrfGfqlxeNoQEfbgR1PbBDe0b1SWL23oKYvVXZxJbeO7HZC21BKqUDTM6gQ4PEYnl9eyOJd5QD85oyuXDIojdtHdw/I/k7vkXTE/fmrM1mTURCQfSmlVGNpggoBmQXl5Fd4m9nSnJFccXIHfndWDxKiwxv0/Cnz1p3Q/mIiwujVNvaIx576ZHNNUYZSSoUCTVAhYMsB72CvabF2nho36IRHfHhrecYJ73PyxSdx9bCOvHnbCLolx1BcUc2Pe/NPeDtKKRUomqBCQPr+IgCGpkY0+KypqdrGR/Lr07sSF+lgdO8UAN5Ytpeiiqqg7F8ppY5HE5SFKqrc/PPbXXy66SAAneKtqVkZ1SsZh13YnVPK1HnrqahyWxKHUkrVpgnKQq99v5v5qzMBOLldBF0TrElQ7eIjeeKqQbSLj2BPbhn3f7CBkspqS2JRSqnDNEFZwBiDx2P4bnsOAH88rxe/GhCLzcKx8Xq3i2P62H7ERoSRvr+Yj9ZlHf9JSikVQJqggqzc5ea2f63istnfUVBWRaozkl+c1NbqsADokhRTU9q+KavI4miUUq2dJqgg25BVyMGiipr7F/VPbfKo4gM6xDc1rFrb8nb63XygGI8xftuuUkqdKE1QQbbRN7lgfFQY0y7py5VDOzR5mwvvHtXkbRyWEhdBUmw4ZS4307/Kp6xK+0YppayhCSrIftzrHbHh/87rzYjuSSE5J9OpXb3zTFV5DN9lVBxnbaWUCgxNUEFSXFHFIws3sSunFJvASWn+a5bzt9tGdeeOs7zXor7LqKDcpWXnSqng0wQVJLO/3MGyXXlEhdu5+xe9iI3wX0l518kf+W1bAOFhNi4ZmEYXZxhl1YZPNu4//pOUUsrPNEEFQUWVm+W7cgH487VDOK9fO4sjOj4R4RddvdN8zF21j/xSl8URKaVaG01QQfDN1myq3IZebWOPO7dTKDkpyUGvNg6Kyqt57vNteDxa1aeUCh5NUAG2em8+L3yxHYAR3RMtjubEiAjX9IslNiKMVXvy+XTTAatDUkq1IpqgAuyt5XsBGNzJyWVDml5SHmzOSBu3jfZOZvjl5myLo1FKtSZiAtwZs7CwsGYH27ZtC+i+Qs3ugipeWlVEpF24b2QbIsICU1I+bq53sNm54wJzbau82sODX3un4njwrMSAHUeo6tWrV81tp9PZug5eKQvpGVSAVHkM/9lUCsDpHSOb9Yd6VJiNTs4wPMCra4uprNZrUUqpwAvq8NnDhg0L5u7qtXLlSiCw8XyyYT/lUknfjtHcO24I4WHH/i7QlJgec+9l6vz1fj+e2jGNIYN//bCHgy74vjCOiRf0saSTcTD+d0crLCwM2r6UUj/RM6gA8HgMH6zxjgZ+zamdjpucmur6EZ0Dun2AK4d25P/O74XDLnyzNYelO/MCvk+lVOumCSoAVu3NZ19+OUmx4ZzZI8nqcPzCbhN+cVI7bjnTWzDxypKdVLt1nD6lVOBogvKzwvIq/r10DwCXDm5PmD3wf+I3l+0N+D4OGzMwjfYJkRwqrmRdpjZ9KaUCRxOUH3k8hkcWbmJndilJseFc2D81KPudOn99UPYD3jOp0b1TAHhr2V5c1XoWpZQKDE1QfvTJxgNsPlBMYkw4s64eTIwfx9sLJaN6ehPU5gPFPLRwI4HuqqCUap00QfmJx2OY9+M+wDsaeHJshMURBU7npGjuOKs7NoG1GYV8tz3X6pCUUi2QJig/Wborl4NFlaQ6IzmjhRRGHMvYQe258+weALyzMkPPopRSfqcJyk8+XOstK//l4PbYbM23U+6J+MVJ7UiIdrA7p5QNmUVWh6OUamE0QfnB9kMlbMgsIsph57y+ba0OJ2jCw2xcPCANQOeMUkr5nSYoPzhcVn7hgFSiw1tmYUR9Difkb7bm8MXmgxZHo5RqSTRBNdHmA0Ws2pNPVLidcad0tCSG3U9cYsl+AdrGRzKwoxOAPy/eRvp+bepTSvmHJqgmWrTO27R18YBUnFEOi6Oxxp1n9ai5vWynVvQppfxDE1QTbMgs5JttOYhQcy2mNeqUGM3Dlw8AYOWefIujUUq1FJqgGslV7eGp/27B7TGMGZhGqjPSsljGvrDEsn0f1i8tniiHnT25ZbyzInhDLymlWi5NUI30efpB8ktddEuO4fZR3S2NJRRKvMPDbNx5Tg9s4h0bMKek0uqQlFLNnCaoRjDmp+k0rh7WsdX0ezqec/q05YyeyXgMfLLhgNXhKKWaOU1QJ8gYw4K1WWQWlJMYE84ZPZKtDimkXDLQey1u0fr9lLmqLY5GKdWcaYI6QZ9sOMArS3YBcF6/dtj17OkI/dvH0y8tnuKKal5ZsguPR4dAUko1jiaoE/T11mwAOidGc/mQ9hZHE3pEhJvP7IrNJizedJD/rMywOiSlVDOlCaqBXNUeHv84nY1ZRdhtwsyrBxEX2Tr7PR1P37R47hvTF4D312RqU59SqlE0QTXQF5sP8b1vWokhnRJCakijXw3vZHUIPzO8WyL928dTWunmi82HrA5HKdUMaYJqAGMMH633jhjROSma39UaOSEUPH7lIKtDqNPhGYW/255jcSRKqeZIE1QDbMwqYndOKQnRDp69doilnXKbkxHdE3HYhY1ZRSxcl8WmLOv7aymlmg9NUMfh8Rjmr84EvGcEDnvo/cnW7yu0OoQ6RYeHcUaPZIyBv329kxkLNlBZ7bY6LKVUMxF6n7YhpLiiiocWbmL5rjzC7MJFA1KtDqlOv3zxW6tDqNcdZ3WnS1I0ABVVHjbqWZRSqoE0QR3D7C93sGpPPnGRYUy7pB/JsRFWh9TsxEU6+PO1Qxg7yNuBd9VuHUxWKdUwmqDqcaiogh925GCzCU9fM5hTurSxOqRmy2G31Yy4sWBtFnN+2I2r2mNtUEqpkCfGBLanf2FhYc0Otm3bFtB9+dNH28r4em85J7eL4FcDYq0O55jGzfXOZDt3XDuLI6mfMYb3NpeyPMs7iOw5XaK4uGeLine truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x229382bd7f0>"
|
||||
"<Figure size 432x288 with 3 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -472,9 +472,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x2293aeba080>"
|
||||
"<Figure size 432x288 with 3 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -506,14 +506,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x2293974ea20>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -546,9 +546,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x2293afae278>"
|
||||
"<Figure size 432x288 with 3 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -576,8 +576,8 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"input mean, variance: -0.0029, 1.0011\n",
|
||||
"output mean, variance: -0.1203, 2.4076\n"
|
||||
"input mean, variance: -0.0001, 0.9987\n",
|
||||
"output mean, variance: -0.1248, 2.4008\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -604,9 +604,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x22938b38860>"
|
||||
"<Figure size 432x288 with 3 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -616,7 +616,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"output mean, variance: 0.0044, 2.2524\n"
|
||||
"output mean, variance: 0.0002, 2.2471\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -646,8 +646,8 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"linear output mean, variance: 0.2528, 7489.7719\n",
|
||||
"nonlinear output mean, variance: -8.6036, 30495.7586\n"
|
||||
"linear output mean, variance: 0.0117, 7472.2931\n",
|
||||
"nonlinear output mean, variance: -9.1853, 30468.4928\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -682,9 +682,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x22938cae828>"
|
||||
"<Figure size 432x288 with 3 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -727,9 +727,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x22938ddb4e0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -756,9 +756,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x22938e19be0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -783,9 +783,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x22938e7fe80>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -810,9 +810,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x22939316630>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
@@ -290,9 +290,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c66ca7b8>"
|
||||
"<Figure size 432x288 with 3 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -332,9 +332,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c73b0ef0>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -385,9 +385,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c66ca320>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -432,14 +432,14 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Difference in mean x=-0.015, y=43.076\n"
|
||||
"Difference in mean x=0.028, y=43.758\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c786aba8>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -507,9 +507,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c7814710>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -564,9 +564,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c7825630>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -630,9 +630,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c7e77f28>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -680,9 +680,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c7f1df28>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -728,9 +728,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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||||
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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c7f66780>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -785,9 +785,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c7e1e128>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -974,9 +974,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c7e77940>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1093,9 +1093,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c80ed7f0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1206,9 +1206,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c8153550>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1270,9 +1270,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c80174a8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1444,9 +1444,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
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||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c7e10828>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1454,9 +1454,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
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||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c77c3a58>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1464,9 +1464,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c77bea90>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1549,9 +1549,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c817b470>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1649,9 +1649,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c7e0f1d0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1714,9 +1714,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c811c7b8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1794,9 +1794,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c7f28710>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1870,9 +1870,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c7e2e940>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1977,9 +1977,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c7848550>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2048,9 +2048,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c81d66a0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2093,9 +2093,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c81ff630>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2145,9 +2145,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c7ecaa90>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2185,9 +2185,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c7f8f710>"
|
||||
"<Figure size 720x360 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2589,9 +2589,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c7e72cf8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2599,9 +2599,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c7fae198>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2609,9 +2609,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c813bb00>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2688,9 +2688,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c81b9550>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2698,9 +2698,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c7faa940>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2708,9 +2708,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c82723c8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2895,9 +2895,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c7f220b8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3312,9 +3312,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c7842e80>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3399,9 +3399,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c831be10>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3439,9 +3439,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d6c82a98d0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
@@ -705,7 +705,7 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
@@ -715,7 +715,7 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
@@ -725,7 +725,7 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
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||||
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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
@@ -1493,7 +1493,7 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
@@ -1505,7 +1505,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Final P: [0.024 0.043 0.002]\n"
|
||||
"Final P: [0.024 0.041 0.002]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1538,7 +1538,7 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
@@ -1550,7 +1550,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Final P: [0.021 0.02 0.002]\n"
|
||||
"Final P: [0.02 0.02 0.002]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1580,7 +1580,7 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
@@ -1619,7 +1619,7 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
@@ -1631,7 +1631,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Final P: [0.273 0.875 0.004]\n"
|
||||
"Final P: [0.274 0.826 0.004]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1658,7 +1658,7 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
@@ -1670,7 +1670,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Final P: [0.009 0.009 0.001]\n"
|
||||
"Final P: [0.008 0.009 0.001]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1720,7 +1720,7 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
@@ -1751,12 +1751,12 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
|
||||
+38
-38
@@ -319,9 +319,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x20494475208>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -389,9 +389,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x2049614f4e0>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -480,9 +480,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x204944750f0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -532,9 +532,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x204961ee6d8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -564,9 +564,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x20496127208>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -914,9 +914,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x20496294160>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1043,9 +1043,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x204962678d0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1100,9 +1100,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x2049737eb70>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1150,9 +1150,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x20496141f98>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1188,9 +1188,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
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||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x2049743d240>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1321,9 +1321,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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\n",
|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x2049746d3c8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1373,9 +1373,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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\n",
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAABJCAYAAAD4zsucAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAACY5JREFUeJzt3XuMnFUdxvHvg2BJt7Ct0sXsbkGQqxpcJQgkoiioJGKCMakX/rBNaqlEjQh2s4iUFnEpiUYRY9hVWpWLwB80oiESE1FUJFpsiKSARVv3QtlF2y1MaRPqzz/ed+gwe5vdM9PZmT6fpOm8t3N+c7Y9z76XnVVEYGZmluKIehdgZmaNz2FiZmbJHCZmZpbMYWJmZskcJmZmlsxhYmZmyRwmVhOSbpB05xTbL5f08KGsaab9TvceGpGkjZK+mb++QNIz9a7JmoPDxGpO0lslhaQji+si4q6I+MihrqVe/c5FEfFoRJxe7zqsOThMzKqgNCjNDkcOE3uNpO2SvibpSUkFST+WdLykhyS9JOk3khbl+14oaXCC4y+eoOnf53/vlvSypPMlLZP0h5JjQ9IqSf+QtEvSDyQp33aEpOsk7ZA0IumnklrzbcWznuWSBvJjV0k6J38fuyXdVtJPeb/fy4/bI2mzpAsqHKsLJQ1K6pa0E9iQr79U0pa83z9JOqvkmG5JQ/lYPiPponz9eyU9lh/zvKTbJL2xbGyuzMfmJUk3SnpbfsweSfcV9y+p61pJL+Zfk8uneg9lX79r8nEbk3SvpKNLtq/O6xuWtCKv65RKxsuan8PEyn0S+DBwGvBx4CHgWuA4sn8vX55Fm+/P/14YEQsi4rFJ9rsUOAd4F7AU+Gi+fln+54PAycAC4LayY88FTgU+BXwX+DpwMfAOYKmkD0zS51+ALuBNwN3A/aUT6DTekh93IrBS0nuAO4ArgDcDtwO/kDRP0unAF4FzIuKY/L1tz9s5AFxFNsbnAxcBV5b1dQlwNnAesBroAy4HlgDvBD5TVtdxQAfwOaAv778SS/O+TgLOIht3JF0CfJVsTE8BJhtPO0w5TKzc9yPihYgYAh4FHo+Iv0XEfuAB4N017PvmiNgdEf8Gfks2yUM2aX4nIv4ZES8DPcCnyy4t3RgR+yLiYaAA3BMRIyXvY8K6I+LOiPhPRLwaEd8G5gGVTrz/A9ZExP6IeAX4PHB7RDweEQci4ifAfrIAOJC3/XZJR0XE9oh4Lq9hc0T8Oa9hO1kIlU/W6yNiT0Q8BfwdeDgfjzGywC9/f9/I6/od8CuykKjErRExHBH/BR7k4NdgKbAhIp6KiL3A2grbs8OEw8TKvVDy+pUJlhfUsO+dJa/3lvTVDuwo2bYDOBI4vmTdrOqWdLWkrfllnd1AK9l39ZUYjYh9JcsnAlfnl6t25+0tAdojYhvwFeAGYETSzyW15zWcJumXknZK2gN8a4IaZvL+dkVEoWR5B9kYVmKqr8FAybbS12YOE5u1AjC/uCDpDcDiSfZN/WjqYbKJuugE4FVeP6HOWH5/pJvsu+5FEbEQGANUYRPl72sAuCkiFpb8mR8R9wBExN0R8b78vQSwPj/uh8DTwKkRcSzZZcVKa5jIIkktJcsnkI1hiueBzpLlJYntWZNxmNhsPQscLeljko4CriO7jDORUbJLQifPsq97gKsknSRpAdl37vdGxKuzbK/oGLJQGgWOlHQ9cGxCe/3AKknnKtOSj88xkk6X9CFJ84B9ZGcTB0rq2AO8LOkM4AsJNRStlfTGPDAvBe5PbO8+YLmkMyXNB65PrtCaisPEZiW/Vn8l8CNgiOxMZXCSffcCNwF/zC//nDfD7u4Afkb2VNi/yCbjL82y9FK/Jrvf8CzZpaB9JFy+iYi/kt03uQ3YBWwjv4FNFrQ3Ay+SXUpqIzsDAbgG+CzwElkg3TvbGnI78/6HgbuAVRHxdEqDEfEQcCvZvaxtQPEhiv0p7VrzkH85llnzkHQhcGdEdE63b2I/Z5I9CDCvCmeI1gR8ZmJmFZH0ifzS2SKy+z0POkisyGFiZpW6guz+0nNk93uqcW/HmoQvc5mZWTKfmZiZWbJJP5xubGzMpyxmZjZOa2vruJ+D8pmJmZklc5iYmVmyin4Hw8LVrbXpfWVtmj2UIulTLw4TffUuwJT8iTZ11gRzRa1FX23norFbdk+53WcmZmaWzGFiZmbJHCZmZpbMYWJmZskcJmZmlsxhYmZmyRwmZmaWzGFiZmbJHCZmZpbMYWJmZskcJmZmlsxhYmZmyRwmZmaWzGFiZmbJKvoIepv7hkehdwMMjUBHG/Qsh/bF9a6qcsMF6N0CQwXoaIGeLmhvqX9bTaMwDFt6oTAELR3Q1QMt7bXrZ2xb9rqlHVpPqV1/qUaHYUMvjAxBWwcs74HFc7DOBuAwaQL9D8C6fhgcObhu0yOwZiWsuKxuZVWsfyusewIGCwfXbdoOa86GFWfUr61m0b8VeOJcKAweXLl9E5y9Bs5YUb2OtvbDE+te38+uJ2GwRv2leqAf+tfBSEm9j2yClWvgsjlUZ4PwZa4GNzw6PkggW17bl22fy4YL4yd/yJbXbs6216OtZlEck9dN8OTLm9dmZw/VUBgeHyS17C/V6PD4IIFsuW9ttt1mxGHS4Ho3jA+SosERWL/xkJYzY71bxk/+RYMFWL+lPm01i6nGhMIgbFlfnY629E4eJLXoL9WG3vFBUjQyCBvnSJ0NxGHS4IYmCZKigWm219vQNGcLAzM4m6hmW81iujGhMFCdjgpDFe5Xpf5SjUxT78gcqbOBOEwaXEfb1NuXTLO93jqmuTG+ZAY3zqvZVrOYbkxoWVKdjlo6KtyvSv2lapum3rY5UmcDcZg0uJ7l0DlJYHS2QfeyQ1rOjPV0QeckE15nC3R31aetZjHVmNDSCV3d1emoqydrbyrV7C/V8h5om6Tetk5YNkfqbCAOkwbXvjh7aqs8UDrbsvVz/fHg9pbsSavyCa8zXz+TR3qr2VazKI7JuIm+pTN7uqpaj+u2tOftTTJBV7u/VIvbs6e2ygOlrTNb78eDZ0wRMeGGsbGx1zYsXN1am95X1qbZQylQvUsAsqe21m/M7pEsyc9I5kyQ9E2/y3B+g3ygkF2O6k78OZNqtdUsVBjKbn4XBrJLTV3dNfw5k/Uw9izsfR7mt0Prqen91WquGB3ObraPDGSXtpZ1N2yQRF9t56KxW3a/9rq1tXVcZw6TRHMlTOa0CsLEaktM/P+8YTTBXFFr9Q4TX+YyM7NkDhMzM0vmMDEzs2QOEzMzS+YwMTOzZA4TMzNL5jAxM7NkDhMzM0vmMDEzs2QOEzMzS+YwMTOzZA4TMzNL5jAxM7NkDhMzM0vmMDEzs2QV/T4TMzOzIv8+EzMzqwmHiZmZJZv0MpeZmVmlfGZiZmbJHCZmZpbMYWJmZskcJmZmluz/gg0krg09bWkAAAAASUVORK5CYII=\n",
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x204975a69e8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1455,9 +1455,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAABJCAYAAAD4zsucAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAACNpJREFUeJzt3X9sXWUdx/H3BwYiE8avFm27ATKRGP+YMbiQaDDRIAQJxKhhirDhXIJ/4IyGpkbYHGiFiAbUGChkQxYigjLRhKD8MREjGyD4Y4KaOGbXDlpgP1j5IRtf/zjPDaeXdr3tc7t7b/t5/XV7zrnP+T6n6fnc8zzn9igiMDMzy3FIowswM7PW5zAxM7NsDhMzM8vmMDEzs2wOEzMzy+YwMTOzbA4TazhJCyTtlXToOOtXS1pfp32FpIX1aKsZSDo59WlO+vl+SZc2ui6bfeY0ugCziPgv8I5G1zETRMS5ja7BZidfmVhdVD4ZzxQq+O/DrEb+Y7Epk/SMpG5JfwVGJM2R1CHpF5KGJW2VdEVp+w9JekzSHknPSfp+Wl49VHOKpN9LeknS74ATSm18VNL2Mer4eGkff5K0S9IOST+SdHiN/dko6duS/gi8DLxb0jxJt6W2BiRdWxmOk7Qw1blb0vOS7iq1daOk/tTXxyV9pLRutaS7Ja1PffybpNMk9UgaSu87u6quXkmb075+Jem4A/RheXq9VNLDkr4naWf6fZxb2vYUSQ+lGh6U9ON6DSfa7OMwsVxLgPOAY4A3gF8DfwE6gY8BKyV9Im17I3BjRBwNnAr8fJw27wQepwiRa4DJzAHsB76a3ntmquHLk3j/F4AVwFHANuB2YB+wEPgAcDawPG17DfBb4FigC/hhqZ1HgUXAcak/d0s6orT+fOCO9N4ngAco/h47gTXAzVV1XQJcBnSkem6qsT+LgX9SHI/rgdskKa27E9gMHA+sTn03mxKHieW6KSL6I+IV4AygLSLWRMT/IuI/QB9wUdr2dWChpBMiYm9EPFLdmKQFqZ2rIuK1iHiIIqBqEhGPR8QjEbEvIp6hOCmfNYn+rIuILRGxjyIIzgVWRsRIRAwBP6jqz0lAR0S8GhEPl+pYHxEvpDpuAN4GvLe0nz9ExANpP3cDbcB3I+J14GfAyZKOKW1/R0T8PSJGgKuAz453w0KVbRHRFxH7KYLxXcCJpeN8dfpdPQzcN4njZDaKw8Ry9ZdenwR0pCGmXZJ2Ad8ATkzrvwicBjwt6VFJnxyjvQ5gZzppVmyrtZg0XPQbSc9K2gN8h9Iw2RT6cxiwo9Sfm4H2tP5KQMBmSVskXVaq42uSnkrDUruAeVV1PFd6/QrwfDrhV36G0TcllOvaluqqpV/PVl5ExMuldjuAF0vLqvdhNikzatLUGqL8b6f7ga0R8Z4xN4z4N7AkTWx/CrhH0vFVm+0AjpU0txQoC0r7GQGOrGycPp23ld7/E4phoyUR8ZKklcCnM/rzGnBCuoKo7s+zwJdSHR8GHpT0EMWn/26KIbYtEfGGpJ0UwTNV80uvF1BcFT1ftXwydgDHSTqyFChTbcvMVyZWV5uBPWlS/u2SDpX0fklnAEi6WFJbRLwB7Erv2V9uICK2AY8B35J0eDpJn1/a5F/AEZLOk3QY8E2KIaSKo4A9wF5JpwOXT7UzEbGDYk7kBklHSzpE0qmSzkr9+YykrrT5Toog2p9q2AcMA3MkXQ0cPdU6koslvU/SkRRzKveUrmQmrXScV6fjfCajj7PZpDhMrG7Sye18ionnrRSfnG+lGOIBOAfYImkvxWT8RRHx6hhNfY5i4vhFYBXw09I+dlNMqN8KDFBcqZTv7vp6ev9LFPM1d5HnEuBw4B8UgXEPxZUHFHMOm1J/7gO+EhFbKSbT76cIvm3Aq+QPId0BrKMYtjoCuOKAW9fm8xQ3KbwAXEtxrF6rQ7s2C8kPxzJrbpI2Ausj4tZp3s9dwNMRsWo692Mzk69MzGYpSWekYbtDJJ0DXABsaHRd1po8AW82e70T+CXF90y2A5dHxBONLclalYe5zMwsm4e5zMws27jDXLt37/Yli5mZvcW8efPe8p0pX5mYmVk2h4mZmWWr6W6uY66cN/FGU7Fiepo9mCLrP2TMErc0ugATLT5qPQPOFdMtbpnec9Hu63cdcL2vTMzMLJvDxMzMsjlMzMwsm8PEzMyyOUzMzCybw8TMzLI5TMzMLJvDxMzMsjlMzMwsm8PEzMyyOUzMzCybw8TMzLI5TMzMLJvDxMzMstX0L+ht+gwOQ+9aGBiCznboWQYdbY1tq541tbrBEeh9EgZGoHMu9CyCjrmNrsqmZHgQ1vbC0AC0d8KyHmjraP12moTDpIH67oU1fbB96M1lGzbCqhWw/MLGtFXPmlpd31Ow5s+wfeTNZRuegVUfhOWnN6wsm4p7+6BvDQxtf3PZxg2wYhVcuLx122kiihj7oTnlZ8D74Vjjm+rDsQaHYfGlo0/aFV3tsOn22q8G6tVWPWsapQUfjjU4Aos3jA6Siq65sOnC1rpCmdUPxxoehEsXjz5xV7R3we2barsiaLZ2qhzMh2P5GfBNpHft2CdtKJZft+7gt1XPmlpd75NjBwkUy6978uDWYxnW9o594oZi+brrWrOdJuMwaZCBcU7aFf0TrJ+OtupZU6sbGCdIKvonWG9NZGhggvX9rdlOk3GYNEhn+4HXz59g/XS0Vc+aWl3nBENY81toiGvWa++cYP381mynyThMGqRnWTEPMZauduheevDbqmdNra5nUTE3MpauudC96ODWYxmW9RRzEWNp74Kl3a3ZTpNxmDRIR1txh1T1yburvVg+mYnuerVVz5paXcfc4q6t6kDpSstbafJ91mvrKO6Sqj6Bt3cVy2ud7G62dpqM7+bKNNW7uSoGh4uJ7f6hYhipe2ne90zq0VY9awJa8m6uisE02d4/Ugxtdbfo90xm9d1cFcODxeT2UH8xlLS0e+rfD2mmdpJG383lMMmUGyazQguHyUzhMJn5Gh0mHuYyM7NsDhMzM8vmMDEzs2wOEzMzy+YwMTOzbA4TMzPL5jAxM7NsDhMzM8vmMDEzs2wOEzMzy+YwMTOzbA4TMzPL5jAxM7NsDhMzM8vmMDEzs2w1Pc/EzMysws8zMTOzaeEwMTOzbOMOc5mZmdXKVyZmZpbNYWJmZtkcJmZmls1hYmZm2f4PnEMftBAEQV0AAAAASUVORK5CYII=\n",
|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x20497603710>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1498,9 +1498,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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\n",
|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x204975fd240>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1569,9 +1569,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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\n",
|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x204960ff2e8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1645,9 +1645,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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\n",
|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x204961845c0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1655,9 +1655,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
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|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x2049624b7b8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1665,9 +1665,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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\n",
|
||||
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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x204962125f8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1675,9 +1675,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
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|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x2049627d438>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
+22
-19
@@ -239,11 +239,14 @@
|
||||
" },\n",
|
||||
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
|
||||
" \"HTML-CSS\": {\n",
|
||||
" scale:95,\n",
|
||||
" scale: 95,\n",
|
||||
" availableFonts: [],\n",
|
||||
" preferredFont:null,\n",
|
||||
" webFont: \"TeX\",\n",
|
||||
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
|
||||
" },\n",
|
||||
" \"fast-preview\": {\n",
|
||||
" \"disabled\": false\n",
|
||||
" }\n",
|
||||
" });\n",
|
||||
"</script>\n"
|
||||
@@ -286,9 +289,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x134caa11160>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -333,9 +336,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZoAAAD1CAYAAABp0zNYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3Xl4VdW9//H3ykQSkpwQEkgIQxgCCWMIwQlB1GoRqQNar0MdqrXWjretbe3Vam97f71XrW1tra1WqdaBohYErRNqFW1VDAlDIJFgmBISAgROQuZh/f7IIUaakIGc7DN8Xs/Dk5N99tn5LvbJ+WTtvfbaxlqLiIiIt4Q4XYCIiAQ2BY2IiHiVgkZERLxKQSMiIl6loBEREa8K89aG3W63hrOJiAQ4l8tlelpHPRoREfEqBY2IiHiV1w6ddeZyufr92tzcXABycnIGqhxHqB2+JVDaAYHTFrXDt5yoHW63u0/bUo9GRES8SkEjIiJepaARERGvUtCIiIhXKWhERMSrFDQiIuJVChoRkSDyxL92se9I/aD+TAWNiEiQWL2xjLvXbOWpD3YP6s9V0IiIBIGPK2q4/W9bmJs2jO+eN3lQf7aCRkQkwFU3NPO1pzYQExnG76/OJjx0cD/6e/xpxphlxphKY0xBp2U/NcaUGWM2ev4t9m6ZIiLSH21tlu+t2MTeqjoeuiabEXGRg15Db2LtcWBRF8t/ba3N8vx7eWDLEhGRgfDQ2zt4o3A/d1yYydy0BEdq6DForLXrgKpBqEVERAbQO9sPcP/a7VycNYobzkhzrA5jbc/3JzPGpAEvWWune77/KXADUA3kAt+31h7u/JrONz4rLi4eqHpFRKQXKmtb+eGbh0iICuUXZycQGdbj/cl6LT09veOxN2989gdgIpAFlAP393M7IiIywBpbLb98/whtFn5wumtAQ6Y/+nU/Gmvt/mOPjTF/Al460fonc1+GYLi3gz9RO3xPoLRF7RgY1lp++PxmSo608Nj1OZybObJf23H8fjTGmJRO314KFHS3roiIDJ5n1u/huQ2lfPucSf0OmYHWY4/GGLMcWAgkGmNKgbuBhcaYLMACu4BbvFijiIj0Qv6ew/x0zVbOmpzEdz43uBdlnkiPQWOtvaqLxY95oRYREemng0cbufWpPJJdkTxwZRahIc6el+msX+doRETEd7S0tvGtZ/I5XNfE3249g/joCKdL+gwFjYiIn7vvtY95v+QQ939xFtNTXU6X828015mIiB97eUs5D68r4UunjeWyOaOdLqdLChoRET+1o7KGHzy3idlj47lryTSny+mWgkZExA/VNDTz1Sc3EBURyh+umUNEmO9+nOscjYiIn7HW8oPnNrP7UB1Pf+VUkl2DPyNzX/huBIqISJceXlfCq1sr+PEFGZw2YbjT5fRIQSMi4kf+ueMg975axIUzU7jpzPFOl9MrChoRET9RdqSeby3PZ2JSDPdeNhNjfOeizBNR0IiI+IGG5la+/tQGmlra+OO1cxg6xH9OsftPpSIiQey/X9zGplI3D187h4lJMU6X0yfq0YiI+LhnP9rL8vV7+PrCiXx+WrLT5fSZgkZExIdtKXVz5+oCzpyUyPfPn+J0Of2ioBER8VF7DtVx4xMfkRQzhN9eNdunZmTuC52jERHxQQdqGrl22Yc0t7ax/OZTSRjqWzMy94V6NCIiPqa6oZnrl62nsrqRP98wl0kjYp0u6aQoaEREfEhDcys3P5HL9v01/PHaOcweO8zpkk6aDp2JiPiIltY2vr08nw93VvHAlVmcNTnJ6ZIGhHo0IiI+wFrLHasKeH3bfn76halcnJXqdEkDRkEjIuID7nvtY1bk7uXb50zihnn+MYdZbyloREQc9ui7JTz09idcfepYvnveZKfLGXAKGhERB63KL+V//l7IBdOT+fnF0/1mosy+UNCIiDjkH0WV/OC5zZwxcTi/uTLLby/I7ImCRkTEARt2V3Hr0xvITInjketyGBIW6nRJXqOgEREZZB9X1PDlP39EiiuKP395LjF+NOV/fyhoREQGUenhOq5b9iGR4aH85cZTSIwZ4nRJXqegEREZJIeONnLdY+upb2rlyZtOZUxCtNMlDYrA7q+JiPiIo40t3PDnj9jnruepm05lSrJ/z1/WFz32aIwxy4wxlcaYgi6eu80YY40xid4pT0TE/zW2tHLLk7lsK6/moWuyyUlLcLqkQdWbQ2ePA4uOX2iMGQOcB+wZ4JpERAJGa5vluys28s8dh7j3spmckzHS6ZIGXY9BY61dB1R18dSvgR8CdqCLEhEJBNZa7lpdwMtbKrjzwkwumzPa6ZIcYaztOSeMMWnAS9ba6Z7vLwLOtdZ+xxizC8ix1h7s/Bq3292x4eLi4gEsWUTEP6zYepTnCmu5dEo018wInHMy6enpHY9dLlePV5n2eTCAMSYauAM4v6+vFREJFq/sqOO5wlrOSYvk6ukxTpfjqP6MOpsIjAc2eebkGQ3kGWNOsdZWdPWCnJycfheYm5t70tvwBWqHbwmUdkDgtCWQ2vHe3gaWbarhvKkj+cM12YSF+t+VJCfaH263u0/b6nPQWGu3ACOOfd/doTMRkWC0saKRB9e7mZuWwO+umu2XITPQejO8eTnwPjDFGFNqjLnJ+2WJiPif/D2H+eX7bkbHhfHo9TlEhgfu/GV90WOPxlp7VQ/Ppw1YNSIifmpHZQ03Pv4RrsgQ7pgfT1xkuNMl+QzNDCAicpL2HannusfWExoSwk/OjGNYpHoynengoYjISThc28R1y9ZT09DCEzfOJTlGf78fT0EjItJPtY0tfPnxj9hTVcej1+cwbZTL6ZJ8koJGRKQfmlrauPXpPDaXHuHBq2Zz6oThTpfks9THExHpo7Y2y23PbWLd9gPce9lMzp+W7HRJPk09GhGRPrDW8rOXtrFm0z5+tCiDK+aOcbokn6egERHpgwff2sHj/9rFzfPH87WzJjhdjl9Q0IiI9NLTH+7m/rXbWZqdyo8vyMQzDZf0QEEjItILL28p584XCjgnYwT3XDaTkBCFTG8paEREevCvHQf5z79uZM7YYfz+6mzCNX9Zn+h/S0TkBLaUurn5L7mMTxzKY9fPJSpCV/33lYJGRKQbJQeOcsOf1xMfHcFfbjoFV7TmL+sPBY2ISBf2Vzdw7WPrAXjyplMYGRfpcEX+Sxdsiogcx13XzHWPrcdd38zym09jQlJw3yHzZKlHIyLSSX1TKzc98RE7D9byyLVzmDFa85edLPVoREQ8mlvb+MYzeWzYc5iHrs7mjEmJTpcUENSjERGhff6yHz2/mbeKKvmfS6ZzwYwUp0sKGAoaEQl61lp+8XIhK/PL+P55k7nm1HFOlxRQFDQiEvT++E4Jj763kxvOSOOb50xyupyAo6ARkaD2bO5e7nm1iItmjeKuJVM1f5kXKGhEJGi565v57zVbOWPicH75xVmav8xLFDQiErSe+XAPtU2t3HnhVCLC9HHoLfqfFZGg1NjSyrJ/7mR+eiJTR8U5XU5AU9CISFBanb+PAzWNfHWBbl7mbQoaEQk6bW2WR94tYWpKHGfqokyvU9CISND5x8eV7Kg8ylcXTNAos0GgoBGRoPPwuhJS46O4cKau/h8MChoRCSr5ew6zfmcVN545XnfKHCQ9/i8bY5YZYyqNMQWdlv3cGLPZGLPRGPO6MWaUd8sUERkYj6wrIS4yjCvnjnG6lKDRmzh/HFh03LL7rLUzrbVZwEvAXQNdmIjIQNt1sJZXt1bwpdPGMXSIJq8fLMZa2/NKxqQBL1lrp3fx3I+BsdbaWzsvd7vdHRsuLi4+6UJFRE7Wn/KqeXNXPX+4IJFhUaFOl+O30tPTOx67XK4eR1P0O9KNMf8PuA5wA2f3dzsiIoPB3djGP3bVc9bYSIXMIBuoHk2ktfbuzss792hcrv7foS43NxeAnJycfm/DF6gdviVQ2gGB0xZvt+PXa7fzwJvFvPG9BUwaEeuVnwHBsT/cbnfH4970aAZiyMUzwGUDsB0REa+ob2rlL+/v4nOZI7waMtK1fgWNMSa907cXAUUDU46IyMB7fsNeDtc1c8tZE50uJSj1eI7GGLMcWAgkGmNKgbuBxcaYKUAbsBv4mjeLFBHpr9Y2y5/e3cnssfHkjBvmdDlBqcegsdZe1cXix7xQi4jIgHu1oII9VXX81+IMTTfjEF0WKyIBy1rLI+s+IW14NOdNTXa6nKCloBGRgPXhzio2lbr5yvwJhOrumY5R0IhIwHpkXQnDh0Zw+ZzRTpcS1BQ0IhKQivfX8FZRJdednkZkuC7QdJKCRkQC0iPrSogMD+Ha08c5XUrQU9CISMCpcDfwwsYyrsgZQ8LQCKfLCXoKGhEJOH/+105a2yxfOXOC06UIChoRCTA1Dc0888EeLpiRwtjh0U6XIyhoRCTA/HX9XmoaW7hlgXozvkJBIyIBo7m1jWX/3MlpExKYOTre6XLEQ0EjIgHjxU37KHc3cMsCTZ7pSxQ0IhIQ2qebKWHyyBgWTklyuhzpREEjIgHhne0HKKqo4eb5EzR5po9R0IhIQHhkXQkj44ZwcVaq06XIcRQ0IuL3Csrc/OuTQ3x53ngiwvSx5mu0R0TE7z28roSYIWFcfepYp0uRLihoRMSv7a2q4+Ut5Vx96ljiIsOdLke6oKAREb/22Hs7McCX56U5XYp0Q0EjIn7rcG0TKz7ay0VZo0hxRTldjnRDQSMifuupD3ZT39zKVzXdjE9T0IiIX2pobuWJ93dx1uQkMpLjnC5HTkBBIyJ+aWVeGQePNmnyTD+goBERv9PWZnn03RKmp8Zx+sThTpcjPVDQiIjfWVu4n5KDtXx1wURNN+MHFDQi4nceWVfC6GFRLJ6e7HQp0gsKGhHxK7m7qtiw+zBfOXM8YaH6CPMH2ksi4lceXldCfHQ4V8wd43Qp0ks9Bo0xZpkxptIYU9Bp2X3GmCJLine truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x134ca9fae10>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -363,9 +366,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x134cae162b0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -492,9 +495,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x134cb20fda0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -565,9 +568,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x134caea6eb8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -592,9 +595,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x134cbb74978>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -630,9 +633,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x134cbbf74e0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -668,9 +671,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x134ccc52dd8>"
|
||||
"<Figure size 576x144 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -715,9 +718,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x134ccdfcf98>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
+100
-99
@@ -16,7 +16,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -255,7 +255,7 @@
|
||||
"<IPython.core.display.HTML object>"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -302,7 +302,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -370,7 +370,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -397,14 +397,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b96ef38d0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -461,7 +461,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -498,16 +498,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b96f04940>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -544,16 +544,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b972314a8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -587,14 +587,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b96f2b588>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -625,14 +625,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b97d782b0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -665,7 +665,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -688,14 +688,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b97d4e828>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -755,16 +755,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b98ddcba8>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -854,16 +854,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b96f046d8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -904,16 +904,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b9903da90>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -985,16 +985,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b99084080>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1082,14 +1082,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b99069a58>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1109,14 +1109,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b99053748>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1136,14 +1136,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b990f8908>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1151,9 +1151,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b9913b860>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1176,16 +1176,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"execution_count": 19,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b98e519b0>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1193,9 +1193,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b98e71860>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1203,9 +1203,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b98e71c50>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1213,9 +1213,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b98df0dd8>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1223,9 +1223,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b99224d30>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1289,14 +1289,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b993302e8>"
|
||||
"<Figure size 432x288 with 4 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1415,16 +1415,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"execution_count": 21,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b992f20f0>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1446,16 +1446,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"execution_count": 22,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b99410f28>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1501,16 +1501,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"execution_count": 23,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b99233f28>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1553,14 +1553,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b98df0278>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1629,14 +1629,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b98f12198>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1699,7 +1699,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -1708,7 +1708,7 @@
|
||||
"array([0.7, 0.3])"
|
||||
]
|
||||
},
|
||||
"execution_count": 29,
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1738,16 +1738,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"execution_count": 27,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b97d61128>"
|
||||
"<Figure size 288x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1776,7 +1776,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -1786,7 +1786,7 @@
|
||||
" [0.05, 0.95]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 31,
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1819,7 +1819,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -1828,7 +1828,7 @@
|
||||
"array([0.694, 0.306])"
|
||||
]
|
||||
},
|
||||
"execution_count": 32,
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1865,7 +1865,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -1874,7 +1874,7 @@
|
||||
"array([0.802, 0.198])"
|
||||
]
|
||||
},
|
||||
"execution_count": 33,
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1942,7 +1942,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 34,
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -1952,7 +1952,7 @@
|
||||
" [0.013, 0.886]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 34,
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -2005,14 +2005,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b98fd5748>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2098,16 +2098,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"execution_count": 33,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x28b98fd50f0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2141,8 +2141,9 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 38,
|
||||
"execution_count": 34,
|
||||
"metadata": {
|
||||
"scrolled": true,
|
||||
"tags": [
|
||||
"raises-exception"
|
||||
]
|
||||
@@ -2155,8 +2156,8 @@
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-38-e96cb02ea05b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 5\u001b[0m [0.03, 0.97]])\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mimm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mIMMEstimator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mca\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[1;32mC:\\Users\\rlabbe\\Dropbox\\filterpy\\filterpy\\kalman\\IMM.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, filters, mu, M)\u001b[0m\n\u001b[0;32m 90\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfilters\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 91\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx_shape\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 92\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'All filters must have the same state dimension'\u001b[0m\u001b[1;33m)\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[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_shape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m<ipython-input-34-e96cb02ea05b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 5\u001b[0m [0.03, 0.97]])\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mimm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mIMMEstimator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mca\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[1;32mC:\\Users\\rlabbe\\Dropbox\\filterpy\\filterpy\\kalman\\IMM.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, filters, mu, M)\u001b[0m\n\u001b[0;32m 112\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfilters\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 113\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx_shape\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 114\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'All filters must have the same state dimension'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 115\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 116\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_shape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mValueError\u001b[0m: All filters must have the same state dimension"
|
||||
]
|
||||
}
|
||||
@@ -2251,7 +2252,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.4"
|
||||
"version": "3.6.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@@ -246,11 +246,14 @@
|
||||
" },\n",
|
||||
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
|
||||
" \"HTML-CSS\": {\n",
|
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" scale:95,\n",
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" scale: 95,\n",
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" availableFonts: [],\n",
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" preferredFont:null,\n",
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" webFont: \"TeX\",\n",
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" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
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||||
" },\n",
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" \"fast-preview\": {\n",
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" \"disabled\": false\n",
|
||||
" }\n",
|
||||
" });\n",
|
||||
"</script>\n"
|
||||
|
||||
@@ -232,11 +232,14 @@
|
||||
" },\n",
|
||||
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
|
||||
" \"HTML-CSS\": {\n",
|
||||
" scale:95,\n",
|
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" scale: 95,\n",
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" availableFonts: [],\n",
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" preferredFont:null,\n",
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" webFont: \"TeX\",\n",
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" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
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" },\n",
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" \"fast-preview\": {\n",
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" \"disabled\": false\n",
|
||||
" }\n",
|
||||
" });\n",
|
||||
"</script>\n"
|
||||
|
||||
@@ -239,11 +239,14 @@
|
||||
" },\n",
|
||||
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
|
||||
" \"HTML-CSS\": {\n",
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" scale:95,\n",
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" scale: 95,\n",
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" availableFonts: [],\n",
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" preferredFont:null,\n",
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" webFont: \"TeX\",\n",
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" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
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" },\n",
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" \"fast-preview\": {\n",
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" \"disabled\": false\n",
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" }\n",
|
||||
" });\n",
|
||||
"</script>\n"
|
||||
@@ -286,9 +289,9 @@
|
||||
"outputs": [
|
||||
{
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"data": {
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truncated
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||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x232851c4f98>"
|
||||
"<Figure size 432x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
@@ -239,11 +239,14 @@
|
||||
" },\n",
|
||||
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
|
||||
" \"HTML-CSS\": {\n",
|
||||
" scale:95,\n",
|
||||
" scale: 95,\n",
|
||||
" availableFonts: [],\n",
|
||||
" preferredFont:null,\n",
|
||||
" webFont: \"TeX\",\n",
|
||||
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
|
||||
" },\n",
|
||||
" \"fast-preview\": {\n",
|
||||
" \"disabled\": false\n",
|
||||
" }\n",
|
||||
" });\n",
|
||||
"</script>\n"
|
||||
@@ -290,9 +293,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x24e99b0a9b0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -572,9 +575,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x24e9a520ac8>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
@@ -26,24 +26,6 @@
|
||||
"@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
|
||||
"@import url('http://fonts.googleapis.com/css?family=Lora');\n",
|
||||
"\n",
|
||||
"//@import url('http://fonts.googleapis.com/css?family=Open+Sans');\n",
|
||||
"//@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
|
||||
"//@import url('http://fonts.googleapis.com/css?family=Karla');\n",
|
||||
"//@import url('http://fonts.googleapis.com/css?family=Poppins');\n",
|
||||
"//@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
|
||||
"//@import url('http://fonts.googleapis.com/css?family=Roboto');\n",
|
||||
"//@import url('http://fonts.googleapis.com/css?family=Lato');\n",
|
||||
"//@import url('http://fonts.googleapis.com/css?family=Domine');\n",
|
||||
"//@import url('http://fonts.googleapis.com/css?family=Chivo');\n",
|
||||
"//@import url('http://fonts.googleapis.com/css?family=Cardo');\n",
|
||||
"//@import url('http://fonts.googleapis.com/css?family=Arvo');\n",
|
||||
"//@import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
|
||||
"//@import url('http://fonts.googleapis.com/css?family=Ubuntu');\n",
|
||||
"//@import url('http://fonts.googleapis.com/css?family=Fontin');\n",
|
||||
"//@import url('http://fonts.googleapis.com/css?family=Raleway');\n",
|
||||
"//@import url('http://fonts.googleapis.com/css?family=Merriweather');\n",
|
||||
"\n",
|
||||
"\n",
|
||||
".CodeMirror pre {\n",
|
||||
" font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
|
||||
"}\n",
|
||||
@@ -54,24 +36,8 @@
|
||||
" }\n",
|
||||
" div.text_cell_render{\n",
|
||||
" font-family: 'Lora';\n",
|
||||
" //font-family: 'Open Sans';\n",
|
||||
" //font-family: 'Karla',verdana,arial,sans-serif;\n",
|
||||
" //font-family: 'Roboto',verdana,arial,sans-serif;\n",
|
||||
" //font-family: 'Lato',verdana,arial,sans-serif;\n",
|
||||
" //font-family: 'Domine',verdana,arial,sans-serif;\n",
|
||||
" //font-family: 'Chivo',verdana,arial,sans-serif;\n",
|
||||
" //font-family: 'Cardo',verdana,arial,sans-serif;\n",
|
||||
" //font-family: 'Arvo',verdana,arial,sans-serif;\n",
|
||||
" //font-family: 'Poppins',verdana,arial,sans-serif;\n",
|
||||
" //font-family: 'Ubuntu',verdana,arial,sans-serif;\n",
|
||||
" //font-family: 'Fontin',verdana,arial,sans-serif;\n",
|
||||
" //font-family: 'Raleway',verdana,arial,sans-serif;\n",
|
||||
" //font-family: 'Merriweather',verdana,arial,sans-serif;\n",
|
||||
" //font-family: 'Crimson Text', verdana,arial,sans-serif;\n",
|
||||
" //font-family: verdana,arial,sans-serif;\n",
|
||||
" //font-family: arial,sans-serif;\n",
|
||||
" line-height: 125%;\n",
|
||||
" font-size: 130%;\n",
|
||||
" font-size: 100%;\n",
|
||||
" text-align: justify;\n",
|
||||
" text-justify:inter-word;\n",
|
||||
" }\n",
|
||||
@@ -79,8 +45,7 @@
|
||||
" background: transparent;\n",
|
||||
" color: #000000;\n",
|
||||
" font-weight: 400;\n",
|
||||
" font-size: 12pt;\n",
|
||||
" //font-style: bold;\n",
|
||||
" font-size: 11pt;\n",
|
||||
" font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
|
||||
" }\n",
|
||||
" h1 {\n",
|
||||
@@ -161,13 +126,13 @@
|
||||
" }\n",
|
||||
" div.output_subarea.output_text.output_pyout {\n",
|
||||
" overflow-x: auto;\n",
|
||||
" overflow-y: scroll;\n",
|
||||
" max-height: 50000px;\n",
|
||||
" overflow-y: visible;\n",
|
||||
" max-height: 5000000px;\n",
|
||||
" }\n",
|
||||
" div.output_subarea.output_stream.output_stdout.output_text {\n",
|
||||
" overflow-x: auto;\n",
|
||||
" overflow-y: scroll;\n",
|
||||
" max-height: 50000px;\n",
|
||||
" overflow-y: visible;\n",
|
||||
" max-height: 5000000px;\n",
|
||||
" }\n",
|
||||
" div.output_wrapper{\n",
|
||||
" margin-top:0.2em;\n",
|
||||
@@ -274,11 +239,14 @@
|
||||
" },\n",
|
||||
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
|
||||
" \"HTML-CSS\": {\n",
|
||||
" scale:95,\n",
|
||||
" scale: 95,\n",
|
||||
" availableFonts: [],\n",
|
||||
" preferredFont:null,\n",
|
||||
" webFont: \"TeX\",\n",
|
||||
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
|
||||
" },\n",
|
||||
" \"fast-preview\": {\n",
|
||||
" \"disabled\": false\n",
|
||||
" }\n",
|
||||
" });\n",
|
||||
"</script>\n"
|
||||
@@ -410,9 +378,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAaMAAAD1CAYAAAD9NZqqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzsvXd8FNe5//85M1u1u1pJSEIFgVAFIbpANIMptjG44hbHNrZvnMQpN07yu/ne5Kb8Um+Sm2+Sm1w7zXFubBzHvdAMmGYMNiBRJEACJFRAHbWVdrV9zvcPISw0s2o7uzO7Ou/XSy+bs6MzD2JGn/M85znPQyilYDAYDAZDSTilDWAwGAwGg4kRg8FgMBSHiRGDwWAwFIeJEYPBYDAUh4kRg8FgMBRHE+ob2Gw2lq7HYDAYUY7VaiXBfD/zjBgMBoOhOEyMGAwGg6E4IQ/TDcZqtYbzdkFTWloKACgqKlLYkrHDbFcGZrsyMNuVwWazyTYX84wYDAaDoThh9YwY6sXp9qKzx4nOXif6XF74BQq/IPT/1y+AAuA5Ap7j+v/Lc9DyHOItRsRbDLCaDOC4oPYvGQzGBIaJ0QTA7fGhtqUbzR296Op1obPXic4eJ7rsTnTYnOiyu+Dy+IK6B0cI4swGTIrtF6cEixEJsUbEW4xItMZgekocEmKNIIQJFoPBEMPEKMpwe3yoae7CoYpWNHT04e9H2nC5zYZQ18MVKO0XuV5nwGvizAbkpMcjJy0BOen9X0ygGAwGwMQooqGUora5G+fq2nCpqQtVDR24crUHlALd3d0AgLi44DweOem2u1B6oRmlF5qvjw0WqLyMSZidNVlBCxkMhlIwMYowvD4/ztS04VhlA46fb0K7rU9pk4JiqEBpNRySjAJmZViRledEQqxRYQsZDEY4YGIUAfQ43Ci90IRjlQ04WdUS9P6OmvH6BFRc6UbFlW58UPEuctMTsHhmOopnpiMzJY6F9BiMKIWJkUrp7HHiw7I6HKtsREX91ZDv+aiVqsZOVDV24h97zyDJGoPimelYPnsqZmUmMWFiMKIIJkYqglKKs7Vt2HmsCp+ca4BfmKAKFICrtj5sP1qF7UerkJEUi41LcrF6/nTEGLRKm8ZgMIKEiZEKcDg9OHC6DjuPVuHK1R6lzYkIrlztwZ+2ncDfd5dh9bxMbFiSi8yUOKXNmtBQSkH7HBB6uiHYeyHYbRB6e6592SDYe0G9HlBBAKgACAIg+AHCATwPEA6E4wCtFpzZAs5sBW+JBWexgpgt4C1WcOZYEJO5/zpGVMHESEFqm7uw82gVDpbVR/U+UChxeXx4/3g13j9ejYJpidi4JA/LCjOg4dkvq1BBKYXQY4OvuQG+5ivwNV2Gr60F1N7TLzShhhBwZgs0SanQpE2BJjUDxN4DarKE/t6MkMHEKMxQSnG0ogHvfHQelZfblTYnqqiob0dFfTus2/VYvzgHdy/PhyVGr7RZEY2k8DQ1QOizK2kUhN4eeHp74Km5AACwdndD0BtgO3/iukBp0jLAWePZ3mKEwMQojJRfasWLu0/jYkOn0qZENTaHG68dOIftn1zE/asKcOfSPOh17FEfLdTvh/dKLTwXzsJz4Rz8XZGxaOLcLnhqLlwXKADgrfHQ5c+CLq8Q2sxsEJ49B2qF/cuEgZqmLvx912mcqm5R2pQJhcPlxYu7y7Dt44t4eG0h1i3MYuG7AAguJ7T11dA21KJz39sQXIEraUQSflsXnMcPw3n8MIhOD13ODOjyC6HLmQkuxqS0eYxBMDEKIc0dvXj5g3IcKr+stCkTms5eJ557twTvfFSJx26di+WFGSx0A0Cw98JdcRqeC2fhrb8EU0dH/3jc+BNBiN7Qn3xwLdmAu5aAwFli+/+sNwC8BuCuJStwHCAI/XtN1xIaqNvVnwAxJBHC39sDwW4DdbnGZRv1uOGuKIO7ogwgBNqpWdDlF0JfMA+8lSW/KA0ToxDQ2ePEawfOYnfJJZaerSKaOuz45T+PICc9Ho/fNg/zclKUNinsUErhu1wDZ+kReCrLQf3+cc1DeB785DRoUvv3Z7RpGeAnJYHoQr9HR71e+Duvwtd05do+VgNorx3EP4YkIErhrb8Eb/0lOD7YCn1+IQxFy6HNymMLFYVgYiQjfr+Atw5V4vWD5+D2ju8lZ4Se6sYufP9vBzA/JwVfuWcRJieYlTYp5AhuF9xnTsBVcgS+tuaRv2EImrSM66KjSZ0CPjlFsf0XotVCMzkNmslpwPxiAED18ePgbJ2Ykhh/XaC8TZcxqtPilMJ9/gzc58+An5QE48Jl0M9bDM4YE+K/CWMwTIxkor6lG//91lFUN3YpbQpjlJyqbsFXf/8+nlw/D7cX50TlitjX1gJX6WG4ykpBPe5Rfx/R6vr3V/JmQZdbAM6kcsHmOAjxiTDML7ouUIKzD55L5/sTMaoqQd0jh/f8HVdh3/MeHPt3QD97IYyLVkCTOiXU1jPAxChoBryhf+4/C58/DGcsGLLi8vjwx62lOHL2Mr62qThqvCRPXTX6PtwNb131qL9HiDHBmz4d1ls2QJuZA6KN7MoWnDEGhsIFMBQuAPX74K2vuZYheBZ+2/CLRurzwXXqGFynjkGbPg0xq26DNmdGVC5Y1AIToyBg3lD0UF7TFhVekq+lEY592+GpPj+q67kYMwzzi6GfNQ/1Dc0AIdDlzgyxleGH8BrosvKgy8oDXX8v/G3NcFeWw3XyEwi9w1c98TbWw/bKX6DNzIFp7UZop2SGx+gJBhOjccC8oehksJf0zH1LkBwfOam//s52OA7ugvvMiVFdr82YDsPiFdDPmAOiufZroHFiHD0ghFzfc4q5aR08F87BVXoEntqqYb/PW1eN7hd+B/2M2YhZswGapImXABNKmBiNkcutNvz3m0dR1Rh9B1eNOg0SYo1IsBhhMmqh4TlwhIDnOBDS383V76fwCwLcXj+6ep3o7HXB5nBFVVXx8po2fOV3OyPCSxLsvej7aA9cJz4ZMTOOaHXQz1kIY9FyaFLSw2ShuiG8BvqCudAXzIWvvRWu0o/hKjs+bPr4QLKDYX4xYlatZ2nhMsHEaAx8eLoOv3/7ODy+yMyU02l4ZKXFITstASkJZiRYjNfFJyHWCMM4qxT4/AJsdld/2/Ge/tbj7bY+1LV0o7qxa9hW5GplwEsqu9SCbzywdNw/m1BBPW70HdkP5ycHQb2eYa/lE5NhXLQC+jlF4AysWWEgNImTYV5/L0xrNsB99hScxz+Cr7Up4PWuU8fgLj8Bw+IViFl5K/vZBom63jCVIggUW/aU4c1DlUqbMmq0PIf0STFYsSAXOekJyE5PQEZSLPgQVCDQ8BwmWWMwySqdCtvV60R1Y+egr8gRqI/PNaCpYw++/9gq1YTtPHXVsG99Ff6ujmGv463xiFm9AfrZC1iV6zFAdHoYFiyBfn4xPJXlcOzfAX/HVclrqd8H5ycH4T57CpY7H4QutyDM1kYPTIxGwOnx4adbDqHkQuAVklrISo1D8cwpWDQjDZ1NNeA5gqKiIqXNQrzFiEUz0rFoxqehoc4eJ05WNeN4ZSNOVjWr+lxWXYsN33huN77zyAoUTk9WzA7qccOxbzucxw8Pex0XY0bMyltgWLjs0/0gxpghhEBfMBe6/EK4y0rgOLgLQq9N8lqh1wbbK8/DMHcRTOvvZV7SOGBP6jBctbnwwt4quIlBaVMk0fAc5mZPxuIZ6Vg8Mx2JgzyT0pZaBS0bmYRYI9YtzMK6hVnweP0or2nFsYoGHD/fhO7ubqXNE9HT58b3XtiPp+8qwvrFOWG//2i8IaLVwbhsNYxLb+4vu8OQBcLz/Z7S7IVwHv8IzsN7A9buc5WVwFNzkXlJ44CJUQBOV7fgt9sq4PT4ERennhebIwRLCtKxam4m5uemwKiP7LMgAKDT8ijKT0NRfhq+TCne2XUIZy93obqDoss+vjpkocAvUDz3bglqm7vw+TsWhqXoar83tAPO4x8FvIbwPAxFyxFz0y3qP5wawRCtFjHL18CwYAmcR/bDeexDUJ+4BBHzksaHbGJECOEBlAJopJTeIde84YZSiq1HLuCF90/B6VFP6CjBYsT6xdm4tSg74N5MNEAIwdQkE6YmmTBv/gIcrWjAzqNVOFPbprRp19l5rBpX2nrw7c+uQKwpdLXYRuMNaTMyYb7rYWgSlQsfTjQ4YwxM6+6AYdFy2Le+Ck/NRcnrmJc0NuT0jJ4BUAkgVsY5w4ogUPzhvRLsLrmktCnXmZOVjI1L8rB4ZvqEa3+g4TmsmD0VK2ZPxZU2G3YercK+k7VwqqAr7pnaNnzzD7vx4ydXIy1R3g6jlFI4D++DY/+OgNcQjQamNRthKF7JkhMUgrfGI/bRp+E6eRSOPe9Jllsa8JJilq1BzNqN7N9qGAiV4YAIIWQKgBcB/AzANwd7Rjab7foNqqqGP1SmJH6B4pVDNThZo/z5IQ1PsCQvCctnJiMljrn4g3F5/Th5qQMHzragvWf0tdZCRaxRiy/dni/fv5PPi5ijB6CrD/yu+JJS0LdkLYRYdr5FLRBHL2KOHoC25UrAa7xp0+BYfgsQhsrm4SI3N/f6/1ut1qAO5MklRm8C+DkAC4B/izQx8vkFbPmwBuV1ypb1IQRYlJOI9fPTEG+Ongc2FPj8Ao5XtWPP6SbY+ryK2mI2aPD0+nykJwQXPiUOO0yHdkLTGSCNmNfANa8Y7rw5/X2AGOqCUuguVcB48giIV/qZ9MfGw7FqQ9QsJFQlRoSQOwBsoJR+mRByM4YRI6vVGtS9QoHH68cvXjksmbo9kNUVF0SzsdGypCAdm2+di4xkeX5GpaWlAKCK1O6xMhbb3R4ftn1yEW9+WAGHSzlRMht1+PGTN8N2LYtxrD937+Va9Lz+NwgOu+Tn2oxMWO7+LPhJSUHbGoiJ8syEGr+ta9i9JGIwIPb+x6HLngFAXbaPFZvLine truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d4f4a8ee10>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -528,7 +496,7 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAAsAAAATBAMAAAC0B+rjAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIokQdkQymVRmzbur\n3e+SS/cOAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAYElEQVQIHWNgYGBgBGIgYEKhTCC8MAilAKLY\nAgsbgVQF4wQuAQbuCZwCTAcY5gK1sV9g2A6kuA4wvGSQZJBvYFjCMBEowhDLoMBdANRWuaUMpJ1x\nAohk4BQAU1DbOEEcAFCSDm4gARWbAAAAAElFTkSuQmCC\n",
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAAsAAAATBAMAAAC0B+rjAAAAMFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIokQdkQymVRmzbur3e+SS/cOAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAYElEQVQIHWNgYGBgBGIgYEKhTCC8MAilAKLYAgsbgVQF4wQuAQbuCZwCTAcY5gK1sV9g2A6kuA4wvGSQZJBvYFjCMBEowhDLoMBdANRWuaUMpJ1xAohk4BQAU1DbOEEcAFCSDm4gARWbAAAAAElFTkSuQmCC\n",
|
||||
"text/plain": [
|
||||
"\\phi"
|
||||
]
|
||||
@@ -561,7 +529,7 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAACkAAAAwBAMAAAB+l06WAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAVO8Qq5l2zWYiuzKJ\nRN0MreaOAAAACXBIWXMAAA7EAAAOxAGVKw4bAAABN0lEQVQ4EWNgwA8YlbHIm4R8xiLKwDZ8RVmd\nvntg8/LQEPuPBXygj9PnQ6xGs8wXyOcEi02NWg2T4ywAsnhAPGYHhvMXoMKsAjBRFgMGJgWoqB2I\nNgMRTA8YWH6DGEAQCCJugwj2z3BR5gcggQMgAgj4oGmUp4GBgftmpCRYkOH+AQgNMjaQs4F9Apj7\nBCLIkMHAwNXAMYHHAcRnDWBg4AU57gEDQx/QuaxAmoEhFYiTChgY2BIYGJYCRdkdgAKMAQwzOc3+\nAi0FcjQYZjDMFwAytjIwZDIw/2Jg6AFyyhg6geqBgfHmTMgDBob4CQyPgbx7DAe4gNYwsAMDDyg6\nP4DjAJDHHbEsGkjBAMtnPgMQm7MBJgKiuf+IgrkcE5BFGYqKwVxw6CIk5EGWMDBwIERALKjvIYIA\nUQJebishGGcAAAAASUVORK5CYII=\n",
|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
" 1 \n",
|
||||
"────\n",
|
||||
@@ -591,7 +559,7 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAIsAAAAcBAMAAACjVFE3AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\ng/w/6i9+jIWXI72KpwAAAABJRU5ErkJggg==\n",
|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
" ⎛ 2 ⎞\n",
|
||||
"(φ - 1)⋅⎝φ + 1⎠"
|
||||
@@ -620,7 +588,7 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ4AAABLCAMAAAB3JhEVAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\nZfMfmA4D/WMrfyLnD+Kk/1SSWdR/72DNtOJLnxsAAAAASUVORK5CYII=\n",
|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
"⎡ x - xₐ y - yₐ ⎤\n",
|
||||
"⎢ ────────────────────────── 0 ────────────────────────── 0⎥\n",
|
||||
@@ -769,9 +737,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d4f31578d0>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -880,9 +848,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1d4f3151780>"
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
@@ -239,11 +239,14 @@
|
||||
" },\n",
|
||||
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
|
||||
" \"HTML-CSS\": {\n",
|
||||
" scale:95,\n",
|
||||
" scale: 95,\n",
|
||||
" availableFonts: [],\n",
|
||||
" preferredFont:null,\n",
|
||||
" webFont: \"TeX\",\n",
|
||||
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
|
||||
" },\n",
|
||||
" \"fast-preview\": {\n",
|
||||
" \"disabled\": false\n",
|
||||
" }\n",
|
||||
" });\n",
|
||||
"</script>\n"
|
||||
|
||||
@@ -239,11 +239,14 @@
|
||||
" },\n",
|
||||
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
|
||||
" \"HTML-CSS\": {\n",
|
||||
" scale:95,\n",
|
||||
" scale: 95,\n",
|
||||
" availableFonts: [],\n",
|
||||
" preferredFont:null,\n",
|
||||
" webFont: \"TeX\",\n",
|
||||
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
|
||||
" },\n",
|
||||
" \"fast-preview\": {\n",
|
||||
" \"disabled\": false\n",
|
||||
" }\n",
|
||||
" });\n",
|
||||
"</script>\n"
|
||||
@@ -267,7 +270,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
@@ -276,7 +279,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
@@ -285,7 +288,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
|
||||
@@ -6,5 +6,4 @@ cp -r ..\kf_book\ .\tmp\
|
||||
cd tmp
|
||||
|
||||
forfiles /m *.ipynb /c "cmd /c ipython ..\rm_notebook.py @file"
|
||||
forfiles /m *.ipynb /c "cmd /c jupyter nbconvert --to notebook --execute @file --output @file"
|
||||
|
||||
jupyter nbconvert --allow-errors --inplace --execute *.ipynb
|
||||
+39
-44
@@ -148,7 +148,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -158,33 +158,37 @@
|
||||
"text/html": [
|
||||
"<style>\n",
|
||||
"@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
|
||||
"@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
|
||||
"@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
|
||||
"@import url('http://fonts.googleapis.com/css?family=Fira+sans');\n",
|
||||
"@import url('http://fonts.googleapis.com/css?family=Lora');\n",
|
||||
"\n",
|
||||
".CodeMirror pre {\n",
|
||||
" font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
|
||||
"}\n",
|
||||
" div.cell{\n",
|
||||
" width: 900px;\n",
|
||||
" //width: 950px;\n",
|
||||
" margin-left: 0% !important;\n",
|
||||
" margin-right: auto;\n",
|
||||
" }\n",
|
||||
" div.text_cell_render{\n",
|
||||
" font-family: 'Lora';\n",
|
||||
" line-height: 125%;\n",
|
||||
" font-size: 100%;\n",
|
||||
" text-align: justify;\n",
|
||||
" text-justify:inter-word;\n",
|
||||
" }\n",
|
||||
" div.text_cell code {\n",
|
||||
" background: transparent;\n",
|
||||
" color: #000000;\n",
|
||||
" font-weight: 600;\n",
|
||||
" font-weight: 400;\n",
|
||||
" font-size: 11pt;\n",
|
||||
" font-style: bold;\n",
|
||||
" font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
|
||||
" }\n",
|
||||
" h1 {\n",
|
||||
" font-family: 'Open sans',verdana,arial,sans-serif;\n",
|
||||
"\t}\n",
|
||||
"\t\n",
|
||||
"\n",
|
||||
" div.input_area {\n",
|
||||
" background: #F6F6F9;\n",
|
||||
" border: 1px solid #586e75; \n",
|
||||
" border: 1px solid #586e75;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .text_cell_render h1 {\n",
|
||||
@@ -197,7 +201,7 @@
|
||||
" display: block;\n",
|
||||
" white-space: wrap;\n",
|
||||
" text-align: left;\n",
|
||||
" } \n",
|
||||
" }\n",
|
||||
" h2 {\n",
|
||||
" font-family: 'Open sans',verdana,arial,sans-serif;\n",
|
||||
" text-align: left;\n",
|
||||
@@ -213,7 +217,7 @@
|
||||
" display: block;\n",
|
||||
" white-space: wrap;\n",
|
||||
" text-align: left;\n",
|
||||
" } \n",
|
||||
" }\n",
|
||||
" h3 {\n",
|
||||
" font-family: 'Open sans',verdana,arial,sans-serif;\n",
|
||||
" }\n",
|
||||
@@ -254,23 +258,15 @@
|
||||
" display: block;\n",
|
||||
" white-space: nowrap;\n",
|
||||
" }\n",
|
||||
" div.text_cell_render{\n",
|
||||
" font-family: 'Vollkorn', verdana,arial,sans-serif;\n",
|
||||
" line-height: 150%;\n",
|
||||
" font-size: 130%;\n",
|
||||
" font-weight: 400;\n",
|
||||
" text-align:justify;\n",
|
||||
" text-justify:inter-word;\n",
|
||||
" }\n",
|
||||
" div.output_subarea.output_text.output_pyout {\n",
|
||||
" overflow-x: auto;\n",
|
||||
" overflow-y: scroll;\n",
|
||||
" max-height: 50000px;\n",
|
||||
" overflow-y: visible;\n",
|
||||
" max-height: 5000000px;\n",
|
||||
" }\n",
|
||||
" div.output_subarea.output_stream.output_stdout.output_text {\n",
|
||||
" overflow-x: auto;\n",
|
||||
" overflow-y: scroll;\n",
|
||||
" max-height: 50000px;\n",
|
||||
" overflow-y: visible;\n",
|
||||
" max-height: 5000000px;\n",
|
||||
" }\n",
|
||||
" div.output_wrapper{\n",
|
||||
" margin-top:0.2em;\n",
|
||||
@@ -288,42 +284,38 @@
|
||||
" margin: 2em;\n",
|
||||
" }\n",
|
||||
" ul li{\n",
|
||||
" padding-left: 0.5em; \n",
|
||||
" margin-bottom: 0.5em; \n",
|
||||
" margin-top: 0.5em; \n",
|
||||
" padding-left: 0.5em;\n",
|
||||
" margin-bottom: 0.5em;\n",
|
||||
" margin-top: 0.5em;\n",
|
||||
" }\n",
|
||||
" ul li li{\n",
|
||||
" padding-left: 0.2em; \n",
|
||||
" margin-bottom: 0.2em; \n",
|
||||
" margin-top: 0.2em; \n",
|
||||
" padding-left: 0.2em;\n",
|
||||
" margin-bottom: 0.2em;\n",
|
||||
" margin-top: 0.2em;\n",
|
||||
" }\n",
|
||||
" ol{\n",
|
||||
" margin: 2em;\n",
|
||||
" }\n",
|
||||
" ol li{\n",
|
||||
" padding-left: 0.5em; \n",
|
||||
" margin-bottom: 0.5em; \n",
|
||||
" margin-top: 0.5em; \n",
|
||||
" padding-left: 0.5em;\n",
|
||||
" margin-bottom: 0.5em;\n",
|
||||
" margin-top: 0.5em;\n",
|
||||
" }\n",
|
||||
" ul li{\n",
|
||||
" padding-left: 0.5em; \n",
|
||||
" margin-bottom: 0.5em; \n",
|
||||
" margin-top: 0.2em; \n",
|
||||
" padding-left: 0.5em;\n",
|
||||
" margin-bottom: 0.5em;\n",
|
||||
" margin-top: 0.2em;\n",
|
||||
" }\n",
|
||||
" a:link{\n",
|
||||
" font-weight: bold;\n",
|
||||
" color:#447adb;\n",
|
||||
" }\n",
|
||||
" a:visited{\n",
|
||||
" font-weight: bold;\n",
|
||||
" color: #1d3b84;\n",
|
||||
" }\n",
|
||||
" a:hover{\n",
|
||||
" font-weight: bold;\n",
|
||||
" color: #1d3b84;\n",
|
||||
" }\n",
|
||||
" a:focus{\n",
|
||||
" font-weight: bold;\n",
|
||||
" color:#447adb;\n",
|
||||
" }\n",
|
||||
" a:active{\n",
|
||||
@@ -331,10 +323,10 @@
|
||||
" color:#447adb;\n",
|
||||
" }\n",
|
||||
" .rendered_html :link {\n",
|
||||
" text-decoration: underline; \n",
|
||||
" text-decoration: underline;\n",
|
||||
" }\n",
|
||||
" .rendered_html :hover {\n",
|
||||
" text-decoration: none; \n",
|
||||
" text-decoration: none;\n",
|
||||
" }\n",
|
||||
" .rendered_html :visited {\n",
|
||||
" text-decoration: none;\n",
|
||||
@@ -347,7 +339,7 @@
|
||||
" }\n",
|
||||
" .warning{\n",
|
||||
" color: rgb( 240, 20, 20 )\n",
|
||||
" } \n",
|
||||
" }\n",
|
||||
" hr {\n",
|
||||
" color: #f3f3f3;\n",
|
||||
" background-color: #f3f3f3;\n",
|
||||
@@ -381,11 +373,14 @@
|
||||
" },\n",
|
||||
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
|
||||
" \"HTML-CSS\": {\n",
|
||||
" scale:95,\n",
|
||||
" scale: 95,\n",
|
||||
" availableFonts: [],\n",
|
||||
" preferredFont:null,\n",
|
||||
" webFont: \"TeX\",\n",
|
||||
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
|
||||
" },\n",
|
||||
" \"fast-preview\": {\n",
|
||||
" \"disabled\": false\n",
|
||||
" }\n",
|
||||
" });\n",
|
||||
"</script>\n"
|
||||
@@ -394,7 +389,7 @@
|
||||
"<IPython.core.display.HTML object>"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
||||
Reference in new issue
Block a user