Made covarinace ellipse transparent.
The solid fill made the plots harder to read, so I set them to no fill color.
This commit is contained in:
Roger Labbe committed
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commit
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1 file changed
+64
-64
@@ -391,7 +391,7 @@
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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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"<matplotlib.figure.Figure at 0x448d400>"
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"<matplotlib.figure.Figure at 0x442f400>"
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]
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},
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"metadata": {},
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@@ -560,7 +560,7 @@
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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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"<matplotlib.figure.Figure at 0x448d390>"
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"<matplotlib.figure.Figure at 0x442f2e8>"
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]
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},
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"metadata": {},
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@@ -595,9 +595,9 @@
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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": [
|
||||
"<matplotlib.figure.Figure at 0x8204a58>"
|
||||
"<matplotlib.figure.Figure at 0x8562898>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -634,7 +634,7 @@
|
||||
"data": {
|
||||
"image/png": 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||||
"text/plain": [
|
||||
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||||
"<matplotlib.figure.Figure at 0x8b5fa58>"
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]
|
||||
},
|
||||
"metadata": {},
|
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@@ -672,7 +672,7 @@
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"data": {
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|
||||
"text/plain": [
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||||
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|
||||
"<matplotlib.figure.Figure at 0x8b6a208>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -705,7 +705,7 @@
|
||||
"data": {
|
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||||
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|
||||
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||||
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|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -783,7 +783,7 @@
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"data": {
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||||
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|
||||
"<matplotlib.figure.Figure at 0x7f82f60>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -819,7 +819,7 @@
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||||
"data": {
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||||
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"data": {
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
@@ -916,7 +916,7 @@
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||||
"data": {
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||||
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||||
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||||
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -947,7 +947,7 @@
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"data": {
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x81d7748>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -978,7 +978,7 @@
|
||||
"data": {
|
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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8df1fd0>"
|
||||
"<matplotlib.figure.Figure at 0x8ad7390>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1016,7 +1016,7 @@
|
||||
"data": {
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x95c8860>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1047,7 +1047,7 @@
|
||||
"data": {
|
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||||
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||||
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||||
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||||
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|
||||
},
|
||||
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||||
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||||
"data": {
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"text/plain": [
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||||
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|
||||
"<matplotlib.figure.Figure at 0x90fa320>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1121,7 +1121,7 @@
|
||||
"data": {
|
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||||
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||||
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||||
"<matplotlib.figure.Figure at 0x985eb00>"
|
||||
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|
||||
},
|
||||
"metadata": {},
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||||
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||||
"data": {
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x8b03f60>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1203,7 +1203,7 @@
|
||||
"data": {
|
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||||
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||||
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|
||||
@@ -1268,7 +1268,7 @@
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||||
"data": {
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||||
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||||
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|
||||
},
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||||
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"data": {
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||||
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|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1326,7 +1326,7 @@
|
||||
"data": {
|
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||||
"text/plain": [
|
||||
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||||
"<matplotlib.figure.Figure at 0x90afa90>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1355,7 +1355,7 @@
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||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x8c8cb70>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1442,7 +1442,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x87fd128>"
|
||||
"<matplotlib.figure.Figure at 0x8665c50>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1920,7 +1920,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8e05da0>"
|
||||
"<matplotlib.figure.Figure at 0x95ea588>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2233,9 +2233,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8e79668>"
|
||||
"<matplotlib.figure.Figure at 0x8b48390>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2245,7 +2245,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8e07940>"
|
||||
"<matplotlib.figure.Figure at 0x8c53160>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2292,9 +2292,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x836feb8>"
|
||||
"<matplotlib.figure.Figure at 0x909a438>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2347,9 +2347,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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||||
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||||
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||||
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||||
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|
||||
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|
||||
"metadata": {},
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"data": {
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||||
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||||
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2468,7 +2468,7 @@
|
||||
"data": {
|
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||||
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||||
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||||
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||||
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|
||||
},
|
||||
"metadata": {},
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@@ -2527,7 +2527,7 @@
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"data": {
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|
||||
"text/plain": [
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|
||||
"<matplotlib.figure.Figure at 0x86a6470>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2557,7 +2557,7 @@
|
||||
"data": {
|
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||||
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|
||||
},
|
||||
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"data": {
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||||
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|
||||
"<matplotlib.figure.Figure at 0x7f970b8>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2658,7 +2658,7 @@
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||||
"data": {
|
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||||
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||||
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|
||||
},
|
||||
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|
||||
@@ -2755,7 +2755,7 @@
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||||
"data": {
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|
||||
},
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||||
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"data": {
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||||
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||||
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2809,7 +2809,7 @@
|
||||
"data": {
|
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||||
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||||
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|
||||
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||||
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|
||||
},
|
||||
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||||
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||||
"data": {
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||||
"text/plain": [
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||||
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|
||||
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|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2853,7 +2853,7 @@
|
||||
"data": {
|
||||
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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8955a90>"
|
||||
"<matplotlib.figure.Figure at 0x8558908>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2887,7 +2887,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8808898>"
|
||||
"<matplotlib.figure.Figure at 0x858e860>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2917,16 +2917,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 54,
|
||||
"execution_count": 64,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x92cb470>"
|
||||
"<matplotlib.figure.Figure at 0x910f0f0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2934,9 +2934,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEmCAYAAAAumVV6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd8VFXawPHflEzNpHcSSiABQpOOSAkIKLpWeFfhRUCl\nyKJie1F2VVBXWVaXZd1VbFhWxe7iqquCSwCVIkgNLYSShPSeycxk6n3/CAzGhKJOCvB8NR/Jveee\n89zLmDw5OfMclaIoCkIIIYQQQohWpW7tAIQQQgghhBCSmAshhBBCCNEmSGIuhBBCCCFEGyCJuRBC\nCCGEEG2AJOZCCCGEEEK0AZKYCyGEEEII0QZoWzsAIYQQ4qe2bNnCd999h9VqZdOmTfz+979nxIgR\nrR2WEEI0K0nMhRBCtCl2u51Vq1axePFiAD766CPGjx/PoUOHSEhIaOXohBCi+chSFiGEEG1KdnY2\nS5Ys4ciRIwCMGzcOh8PBxo0bWzkyIYRoXpKYCyGEaFN69+7Nxo0bSU5OBiAvLw+AlJSU1gxLCCGa\nnUpRFKW1gxBCiAvBpEmTyMrKYseOHZjNZkaMGIFarcZut1NTU0P37t2ZPXs2l112WbPFcOTIEebP\nn09SUhI6nY7c3FwWL15Mx44dz+n6rl278uijj5Keno7ZbGbbtm0sWbKEf/zjH3Tt2rXZ4j6TW265\nhejoaJYuXdoq4wshREuRxFwIIQJo586d9OvXj4cffpjHH3/cf1xRFB544AGWLVvGW2+9xaRJkwI+\nttVqpXfv3jzyyCPcdtttAKxYsYLHH3+cPXv2EBISctY+1OqGv0gNCgpi2bJlzJkzJ+DxnosVK1Zw\n4MABnn766VYZXwghWpIsZRFCiABav349UL8u+sdUKhUzZ85EUZRmSzKffvpprFYrU6dO9R+75ZZb\nqK2tPefZ5vbt2zNjxgyuueYa7r//fnbv3t1qSfnnn3+OWq3m6aefxul0kpOT0ypxCCFES5GqLEII\nEUAbNmwgODiYSy+9tNG5Y8eOARAWFtYsY7/77rsMGjQIrfbUl3adTsegQYN47733WLRo0Vn76NSp\nEy+99FKzxPdzrF+/nuLiYq6++mqKiorYtGkT8fHxdOjQobVDE0KIZiOJuRBCBIiiKGzYsIERI0ag\n0WganX/22WfR6/U89thjAR/barWSnZ3N5Zdf3uhcUlISX331FTabDbPZHPCxA+3IkSNcc8011NbW\n+o+pVCqqq6tbMSohhGh+kpgLIUSA7Nu3j/LycsaOHdvgeGFhIQsWLOD48eNkZGQwZMiQgI99cplH\nU+vIQ0NDASguLvZXOjkdl8vFkiVLKCkpQaPRcPToUf70pz/RuXPnc4rjX//6FxkZGezZs4fXX3+d\nsrIyPvzwQ1QqFRs3buSBBx7gyiuvZNmyZZSUlGC328nLy+PNN9/0x56cnExNTc3PuX0hhLggSGIu\nhBABcnJ9+Z49e1iwYAFut5vvvvuO3NxcXnnlFV5//fXTXjtnzhx27tz5s8Z74IEHmDBhAoA/kdXp\ndI3anTxWWVl51j6LioqYPHkySUlJALz99tsMHz6cnTt3EhMTc8ZrnU4n3333Hc8++ywDBw5kypQp\n3Hjjjf6Ngv785z9z++23c/PNNzN37lxSU1MBSEtLY9myZTz66KPneOdCCHFhksRcCCECZMOGDYSE\nhPDKK6/4j9lsNq688kr++te/Mn78+NNeu3z58l819sl15SqVqtE5p9MJgNvtPms/2dnZDfqYNGkS\ns2bN4qmnnmLZsmVnvHbdunWMGDECRVE4fPgwY8aM4d57720QY3l5OZMmTfIn5VBf+aW0tPSssQkh\nxIVOqrIIIUSArF+/nsGDBzc4Zjabufnmm/n6669Zt25ds40dHR192nM2mw0Ai8Vy1n5+mtir1Wqi\noqL45JNPznptr169uOKKK9i1axdVVVXMmzevwfnvv/+eAQMGNFjKY7Va2b9/P3369Dlr/0IIcaGT\nxFwIIQIgKyuL4uJihg4d2uhcbm4uABUVFc02fmxsLCqVqsnlKjabDZVKddZNhtLT00lLS/PPsJ/k\n8/koLi4+awwJCQno9XrWrl2L0Whs9ENKRkYGo0aNanDs888/x+fzcfXVV5+1fyGEuNDJUhYhhAiA\nk+vLm0rMMzIygDNvKT979my2b9/+s8Z86KGH/GvMTSYT/fv39/8Q8GPZ2dl06dLlrBVZfvjhhyZn\n3ktLS+nUqdM5x7V27VqGDh3aoGzj3r17KS0tbZSYv/vuu6SnpxMfH09ubi7t2rVrsqKNEEJcDCQx\nF0KIANiwYQMajabJiiuZmZmoVCp/RZSSkhJCQkIwGAz+Ni+++OKvjuGaa67hueeeQ1EU/5KUqqoq\nduzYwf3339+g7bFjx4iIiGhQxWX8+PG8+OKL6PV6/7GdO3ficrmYMmXKOcXg9Xr55ptvmD9/foPj\na9euJSgoiGHDhvmPVVdX88UXX/Dqq68CsHjxYp5//vmfd9NCCHEBkaUsQgjxKymKwrp16+jevXuT\n67hPlho8ud39gw8+iNfrDXgcs2fPxu12s2LFCv+xF154gaioKB588EH/scOHD5OSksKgQYMaXD9/\n/nxmzZqFw+Hw39fSpUsZOnRoo0T7dLZt24bVaiU9Pb3B8bVr1zJo0CBMJlODONxuN9dccw07duwg\nLS2tyTevCiHExUJmzIUQ4hc6cuQIs2fPJj8/n4KCAioqKhg1ahQ333wzs2fP9rd7++23ueeee5g7\ndy4qlYrZs2c3y0Y/MTExbNiwgYceeog9e/bgdrvJy8vjv//9b4MfGMLCwkhKSmq0tGbAgAHMmzeP\nadOmYTQaqampoXfv3rzyyisEBQWdUwyFhYX06tWr0frysrIypk2b1uDYJZdcwsSJE3nwwQeJi4uT\ncolCiIueSlEUpbWDEEIIIYQQ4mInS1mEEEIIIYRoAyQxF0IIIYQQog2QxFwIIYQQQog2oE2++bO6\nurq1QxBCCCGEEOIXCw0N/dnXyIy5EEIIIYQQbYAk5kIIIYQQQrQBbXIpy4/9kl8DiKZt27YNqK9V\nLAJHnmvzkWfbPOS5Ng95rs1DnmvzkOfaPH7tcmyZMRdCCCGEEKINkMRcCCGEEEKINkAScyGEEEII\nIdqAgCXmixcvZuDAgYSGhhITE8O1117L3r17G7VbtGgR7dq1w2QyMWrUKPbt2xeoEIQQQgghhDhv\nBSwxX79+PXfeeSebNm1i7dq1aLVaxowZQ2Vlpb/NkiVLWLp0Kf/4xz/YunUrMTExjB07ltra2kCF\nIYQQQgghxHkpYFVZvvzyywafv/nmm4SGhrJx40auvvpqFEVh2bJlLFiwgBtuuAGAN954g5iYGFau\nXMmsWbN+0bg+nw+Xy/Wr478YdOjQAYC6urpWjuTCEqjnqtPpUKtldZkQQghxsWq2cok1NTX4fD7C\nw8MBOHr0KMXFxYwbN87fxmAwMGLECDZu3PiLEnNFUXA6nRgMBlQqVcBiv1AZDIbWDuGCFIjnqigK\ndXV18loWQgghLmLNlpjPmzePvn37cumllwJQVFQEQGxsbIN2MTExFBQU/KIxXC4XOp1OEhlx3lOp\nVOh0OlwuF3q9vrXDEUKIFudTfNS6aht97CjZgdPr5ODugzg8Duo8dTjcDryKF5/iw6f4UBQFAI1a\ng1FrxKK30DGsI+kd09Gq2/yWLUL4Ncur9b777mPjxo18++2355Q0n6nNyQL4TUlMTCQuLu4XxShE\nW6PRaCgtLeX48eOtHUqbc6avA+KXk+faPOS5npmiKFS7qylzllHhrKDaVU2Vq4oadw1exfvTxqi8\nXvJLTCRYs3/WON/zPSXHSkgNSQ1g9Bceeb0GVkpKyq+6PuCJ+b333sv7779PRkYGHTt29B8/mUAX\nFxeTmJjoP15cXPyLk2uNRvOrYhWirZHXtBDiQuX2udlesZ3D1sPUupsu+mDUGDGqDWhcLrx1tdTZ\nKwAV3px0uncvx6f48CpevIoXp8+J1W3F6rY22VecMY4kU1Iz3pEQgRfQxHzevHl88MEHZGRkkJra\n8CfUTp06ERcXx+rVq+nfvz9Q/2a5b7/9lmeeeea0fZ5pq1h5E6O40FgsFtke+Udky+jmIc+1echz\nPbN9pfsotZcSYgghhJAG53weD0EOD47KcmxVeSiKz3/O5gpi45H9OLbq6dgRfjTnh+XEP8G6YKJM\nUcSYY4gPjqdLRBcsekvL3Nh5Sl6vzaO6uvpXXR+wxHzu3Lm89dZbrFq1itDQUP+acovFgtlsRqVS\ncc899/DUU0/RrVs3UlJS+OMf/4jFYmHy5MmBCkMIIYQQbVCCJYGkkCTyavIA8LlcuGqqcVVX4/lJ\n2WSNyYQuNJTg8Bi0VVZ6p4Qx48Z2BOuCG3yEG8KJNEVi0EpxA3FhCFhivnz5clQqFZdffnmD44sW\nLeLRRx8FYP78+TgcDubOnUtlZSVDhgxh9erVmM3mQIUhhBBCiDbA7XVjc9twuB04PA4cbgedTIl4\nyyvJyz9IdXWpv61KpSIsIo7EhFS6dehLWnwvwo3hqFVqtm3bRtcKC1N6d23FuxGiZQQsMff5fGdv\nBCxcuJCFCxcGalghhBBCtDBFUbC5bZTZyyi3l1NmL6PaWe2vpGJz2XB6nSiKQt5hB1HmalzVVfic\nzlOdqFQEhYSgCw0lyBKCSqsln2ry89axrWQHt/e7nRCdhaCyMk6sgBXigic1hNqY119/ndtuuw2A\nDRs2MGzYsEZtunTpwpEjRxg5ciQZGRktHaI4YePGjaxZs4Z77rmH0NDQ1g5HCCGaVbm9nH2l+zhS\neYSi2iIcHsdp23pdTtyVVbgqKgh2Oamz1R/XanWERyVgjojGawjC5rU3eb3dbceoNLine truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8931eb8>"
|
||||
"<matplotlib.figure.Figure at 0x96025f8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2967,7 +2967,7 @@
|
||||
" for i,p in enumerate(cov):\n",
|
||||
" stats.plot_covariance_ellipse(\n",
|
||||
" (i+1, ps[i]), cov=p, variance=4, axis_equal=False, \n",
|
||||
" facecolor='g', edgecolor=None, alpha=0.99)\n",
|
||||
" edgecolor='g', alpha=0.5)\n",
|
||||
"\n",
|
||||
" if i == len(cov)-1:\n",
|
||||
" s = ('$\\sigma^2_{pos} = %.2f$' % p[0,0])\n",
|
||||
@@ -3036,16 +3036,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"execution_count": 65,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x87fda20>"
|
||||
"<matplotlib.figure.Figure at 0x95c6f98>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3196,7 +3196,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x87fd518>"
|
||||
"<matplotlib.figure.Figure at 0x90c1a20>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3277,9 +3277,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8208ba8>"
|
||||
"<matplotlib.figure.Figure at 0x8b4d358>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3329,9 +3329,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8208c50>"
|
||||
"<matplotlib.figure.Figure at 0x869fcf8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3371,9 +3371,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8287470>"
|
||||
"<matplotlib.figure.Figure at 0x869f160>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3438,9 +3438,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8219828>"
|
||||
"<matplotlib.figure.Figure at 0x85850b8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
Reference in new issue
Block a user