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Commit 403df504 authored by d76e800e4e05d8975cb1cb546683fdfa's avatar d76e800e4e05d8975cb1cb546683fdfa
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M2E5 all datat in

parent 1a4cecc5
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Showing with 324 additions and 54 deletions
module2/exo5/exo5_fr.ipynb
View file @ 403df504
...@@ -40,7 +40,7 @@ ...@@ -40,7 +40,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -261,33 +261,33 @@ ...@@ -261,33 +261,33 @@
"</div>" "</div>"
], ],
"text/plain": [ "text/plain": [
" Date Count Temperature Pressure Malfunction\n", " Date Count Temperature Pressure Malfunction\n",
"0 4/12/81 6 66 50 0\n", "0 4/12/81 6 66 50 0\n",
"1 11/12/81 6 70 50 1\n", "1 11/12/81 6 70 50 1\n",
"2 3/22/82 6 69 50 0\n", "2 3/22/82 6 69 50 0\n",
"3 11/11/82 6 68 50 0\n", "3 11/11/82 6 68 50 0\n",
"4 4/04/83 6 67 50 0\n", "4 4/04/83 6 67 50 0\n",
"5 6/18/82 6 72 50 0\n", "5 6/18/82 6 72 50 0\n",
"6 8/30/83 6 73 100 0\n", "6 8/30/83 6 73 100 0\n",
"7 11/28/83 6 70 100 0\n", "7 11/28/83 6 70 100 0\n",
"8 2/03/84 6 57 200 1\n", "8 2/03/84 6 57 200 1\n",
"9 4/06/84 6 63 200 1\n", "9 4/06/84 6 63 200 1\n",
"10 8/30/84 6 70 200 1\n", "10 8/30/84 6 70 200 1\n",
"11 10/05/84 6 78 200 0\n", "11 10/05/84 6 78 200 0\n",
"12 11/08/84 6 67 200 0\n", "12 11/08/84 6 67 200 0\n",
"13 1/24/85 6 53 200 2\n", "13 1/24/85 6 53 200 2\n",
"14 4/12/85 6 67 200 0\n", "14 4/12/85 6 67 200 0\n",
"15 4/29/85 6 75 200 0\n", "15 4/29/85 6 75 200 0\n",
"16 6/17/85 6 70 200 0\n", "16 6/17/85 6 70 200 0\n",
"17 7/29/85 6 81 200 0\n", "17 7/29/85 6 81 200 0\n",
"18 8/27/85 6 76 200 0\n", "18 8/27/85 6 76 200 0\n",
"19 10/03/85 6 79 200 0\n", "19 10/03/85 6 79 200 0\n",
"20 10/30/85 6 75 200 2\n", "20 10/30/85 6 75 200 2\n",
"21 11/26/85 6 76 200 0\n", "21 11/26/85 6 76 200 0\n",
"22 1/12/86 6 58 200 1" "22 1/12/86 6 58 200 1"
] ]
}, },
"execution_count": 1, "execution_count": 2,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -317,12 +317,13 @@ ...@@ -317,12 +317,13 @@
"Les vols où aucun incident n'est relevé n'apportant aucun information\n", "Les vols où aucun incident n'est relevé n'apportant aucun information\n",
"sur l'influence de la température ou de la pression sur les\n", "sur l'influence de la température ou de la pression sur les\n",
"dysfonctionnements, nous nous concentrons sur les expériences où au\n", "dysfonctionnements, nous nous concentrons sur les expériences où au\n",
"moins un joint a été défectueux.\n" "moins un joint a été défectueux.\n",
"Ben voyons !... "
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -355,6 +356,14 @@ ...@@ -355,6 +356,14 @@
" </thead>\n", " </thead>\n",
" <tbody>\n", " <tbody>\n",
" <tr>\n", " <tr>\n",
" <th>0</th>\n",
" <td>4/12/81</td>\n",
" <td>6</td>\n",
" <td>66</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n", " <th>1</th>\n",
" <td>11/12/81</td>\n", " <td>11/12/81</td>\n",
" <td>6</td>\n", " <td>6</td>\n",
...@@ -363,6 +372,54 @@ ...@@ -363,6 +372,54 @@
" <td>1</td>\n", " <td>1</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>2</th>\n",
" <td>3/22/82</td>\n",
" <td>6</td>\n",
" <td>69</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>11/11/82</td>\n",
" <td>6</td>\n",
" <td>68</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4/04/83</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6/18/82</td>\n",
" <td>6</td>\n",
" <td>72</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>8/30/83</td>\n",
" <td>6</td>\n",
" <td>73</td>\n",
" <td>100</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>11/28/83</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>100</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n", " <th>8</th>\n",
" <td>2/03/84</td>\n", " <td>2/03/84</td>\n",
" <td>6</td>\n", " <td>6</td>\n",
...@@ -387,6 +444,22 @@ ...@@ -387,6 +444,22 @@
" <td>1</td>\n", " <td>1</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>11</th>\n",
" <td>10/05/84</td>\n",
" <td>6</td>\n",
" <td>78</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>11/08/84</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n", " <th>13</th>\n",
" <td>1/24/85</td>\n", " <td>1/24/85</td>\n",
" <td>6</td>\n", " <td>6</td>\n",
...@@ -395,6 +468,54 @@ ...@@ -395,6 +468,54 @@
" <td>2</td>\n", " <td>2</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>14</th>\n",
" <td>4/12/85</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>4/29/85</td>\n",
" <td>6</td>\n",
" <td>75</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>6/17/85</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>7/29/85</td>\n",
" <td>6</td>\n",
" <td>81</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>8/27/85</td>\n",
" <td>6</td>\n",
" <td>76</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>10/03/85</td>\n",
" <td>6</td>\n",
" <td>79</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n", " <th>20</th>\n",
" <td>10/30/85</td>\n", " <td>10/30/85</td>\n",
" <td>6</td>\n", " <td>6</td>\n",
...@@ -403,6 +524,14 @@ ...@@ -403,6 +524,14 @@
" <td>2</td>\n", " <td>2</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>21</th>\n",
" <td>11/26/85</td>\n",
" <td>6</td>\n",
" <td>76</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n", " <th>22</th>\n",
" <td>1/12/86</td>\n", " <td>1/12/86</td>\n",
" <td>6</td>\n", " <td>6</td>\n",
...@@ -416,22 +545,38 @@ ...@@ -416,22 +545,38 @@
], ],
"text/plain": [ "text/plain": [
" Date Count Temperature Pressure Malfunction\n", " Date Count Temperature Pressure Malfunction\n",
"0 4/12/81 6 66 50 0\n",
"1 11/12/81 6 70 50 1\n", "1 11/12/81 6 70 50 1\n",
"2 3/22/82 6 69 50 0\n",
"3 11/11/82 6 68 50 0\n",
"4 4/04/83 6 67 50 0\n",
"5 6/18/82 6 72 50 0\n",
"6 8/30/83 6 73 100 0\n",
"7 11/28/83 6 70 100 0\n",
"8 2/03/84 6 57 200 1\n", "8 2/03/84 6 57 200 1\n",
"9 4/06/84 6 63 200 1\n", "9 4/06/84 6 63 200 1\n",
"10 8/30/84 6 70 200 1\n", "10 8/30/84 6 70 200 1\n",
"11 10/05/84 6 78 200 0\n",
"12 11/08/84 6 67 200 0\n",
"13 1/24/85 6 53 200 2\n", "13 1/24/85 6 53 200 2\n",
"14 4/12/85 6 67 200 0\n",
"15 4/29/85 6 75 200 0\n",
"16 6/17/85 6 70 200 0\n",
"17 7/29/85 6 81 200 0\n",
"18 8/27/85 6 76 200 0\n",
"19 10/03/85 6 79 200 0\n",
"20 10/30/85 6 75 200 2\n", "20 10/30/85 6 75 200 2\n",
"21 11/26/85 6 76 200 0\n",
"22 1/12/86 6 58 200 1" "22 1/12/86 6 58 200 1"
] ]
}, },
"execution_count": 2, "execution_count": 3,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"data = data[data.Malfunction>0]\n", "#data = data[data.Malfunction>0]\n",
"data" "data"
] ]
}, },
...@@ -443,17 +588,31 @@ ...@@ -443,17 +588,31 @@
"la pression est quasiment toujours égale à 200, ce qui devrait\n", "la pression est quasiment toujours égale à 200, ce qui devrait\n",
"simplifier l'analyse.\n", "simplifier l'analyse.\n",
"\n", "\n",
"1/3 des experiences n'est pas à 200 psy \n",
"\n",
"Comment la fréquence d'échecs varie-t-elle avec la température ?\n" "Comment la fréquence d'échecs varie-t-elle avec la température ?\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"image/png": 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\n", 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [ "text/plain": [
"<Figure size 432x288 with 1 Axes>" "<Figure size 432x288 with 1 Axes>"
] ]
...@@ -471,6 +630,7 @@ ...@@ -471,6 +630,7 @@
"\n", "\n",
"data[\"Frequency\"]=data.Malfunction/data.Count\n", "data[\"Frequency\"]=data.Malfunction/data.Count\n",
"data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n", "data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n",
"data.plot(x=\"Pressure\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n",
"plt.grid(True)" "plt.grid(True)"
] ]
}, },
...@@ -500,7 +660,7 @@ ...@@ -500,7 +660,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -509,10 +669,10 @@ ...@@ -509,10 +669,10 @@
"<table class=\"simpletable\">\n", "<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n", "<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n", "<tr>\n",
" <th>Dep. Variable:</th> <td>Frequency</td> <th> No. Observations: </th> <td> 7</td> \n", " <th>Dep. Variable:</th> <td>Frequency</td> <th> No. Observations: </th> <td> 23</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 5</td> \n", " <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 21</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 1</td> \n", " <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 1</td> \n",
...@@ -521,16 +681,16 @@ ...@@ -521,16 +681,16 @@
" <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td> 1.0000</td> \n", " <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td> 1.0000</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -2.5250</td> \n", " <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -3.9210</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Date:</th> <td>Sat, 13 Apr 2019</td> <th> Deviance: </th> <td> 0.22231</td> \n", " <th>Date:</th> <td>Wed, 28 Feb 2024</td> <th> Deviance: </th> <td> 3.0144</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Time:</th> <td>19:11:24</td> <th> Pearson chi2: </th> <td> 0.236</td> \n", " <th>Time:</th> <td>20:44:24</td> <th> Pearson chi2: </th> <td> 5.00</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>No. Iterations:</th> <td>4</td> <th> Covariance Type: </th> <td>nonrobust</td>\n", " <th>No. Iterations:</th> <td>6</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
"</tr>\n", "</tr>\n",
"</table>\n", "</table>\n",
"<table class=\"simpletable\">\n", "<table class=\"simpletable\">\n",
...@@ -538,10 +698,10 @@ ...@@ -538,10 +698,10 @@
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Intercept</th> <td> -1.3895</td> <td> 7.828</td> <td> -0.178</td> <td> 0.859</td> <td> -16.732</td> <td> 13.953</td>\n", " <th>Intercept</th> <td> 5.0850</td> <td> 7.477</td> <td> 0.680</td> <td> 0.496</td> <td> -9.570</td> <td> 19.740</td>\n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Temperature</th> <td> 0.0014</td> <td> 0.122</td> <td> 0.012</td> <td> 0.991</td> <td> -0.238</td> <td> 0.240</td>\n", " <th>Temperature</th> <td> -0.1156</td> <td> 0.115</td> <td> -1.004</td> <td> 0.316</td> <td> -0.341</td> <td> 0.110</td>\n",
"</tr>\n", "</tr>\n",
"</table>" "</table>"
], ],
...@@ -550,24 +710,24 @@ ...@@ -550,24 +710,24 @@
"\"\"\"\n", "\"\"\"\n",
" Generalized Linear Model Regression Results \n", " Generalized Linear Model Regression Results \n",
"==============================================================================\n", "==============================================================================\n",
"Dep. Variable: Frequency No. Observations: 7\n", "Dep. Variable: Frequency No. Observations: 23\n",
"Model: GLM Df Residuals: 5\n", "Model: GLM Df Residuals: 21\n",
"Model Family: Binomial Df Model: 1\n", "Model Family: Binomial Df Model: 1\n",
"Link Function: logit Scale: 1.0000\n", "Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -2.5250\n", "Method: IRLS Log-Likelihood: -3.9210\n",
"Date: Sat, 13 Apr 2019 Deviance: 0.22231\n", "Date: Wed, 28 Feb 2024 Deviance: 3.0144\n",
"Time: 19:11:24 Pearson chi2: 0.236\n", "Time: 20:44:24 Pearson chi2: 5.00\n",
"No. Iterations: 4 Covariance Type: nonrobust\n", "No. Iterations: 6 Covariance Type: nonrobust\n",
"===============================================================================\n", "===============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n", " coef std err z P>|z| [0.025 0.975]\n",
"-------------------------------------------------------------------------------\n", "-------------------------------------------------------------------------------\n",
"Intercept -1.3895 7.828 -0.178 0.859 -16.732 13.953\n", "Intercept 5.0850 7.477 0.680 0.496 -9.570 19.740\n",
"Temperature 0.0014 0.122 0.012 0.991 -0.238 0.240\n", "Temperature -0.1156 0.115 -1.004 0.316 -0.341 0.110\n",
"===============================================================================\n", "===============================================================================\n",
"\"\"\"" "\"\"\""
] ]
}, },
"execution_count": 4, "execution_count": 5,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -593,6 +753,89 @@ ...@@ -593,6 +753,89 @@
"estimations avec des pincettes.\n" "estimations avec des pincettes.\n"
] ]
}, },
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Frequency</td> <th> No. Observations: </th> <td> 23</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 21</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 1</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td> 1.0000</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -4.2246</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Wed, 28 Feb 2024</td> <th> Deviance: </th> <td> 3.6216</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>21:05:19</td> <th> Pearson chi2: </th> <td> 3.94</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>6</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> -4.3835</td> <td> 3.487</td> <td> -1.257</td> <td> 0.209</td> <td> -11.219</td> <td> 2.452</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Pressure</th> <td> 0.0102</td> <td> 0.019</td> <td> 0.549</td> <td> 0.583</td> <td> -0.026</td> <td> 0.047</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Frequency No. Observations: 23\n",
"Model: GLM Df Residuals: 21\n",
"Model Family: Binomial Df Model: 1\n",
"Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -4.2246\n",
"Date: Wed, 28 Feb 2024 Deviance: 3.6216\n",
"Time: 21:05:19 Pearson chi2: 3.94\n",
"No. Iterations: 6 Covariance Type: nonrobust\n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept -4.3835 3.487 -1.257 0.209 -11.219 2.452\n",
"Pressure 0.0102 0.019 0.549 0.583 -0.026 0.047\n",
"==============================================================================\n",
"\"\"\""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[\"Success\"]=data.Count-data.Malfunction\n",
"data[\"Intercept\"]=1\n",
"\n",
"logmodel=sm.GLM(data['Frequency'], data[['Intercept','Pressure']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n",
"\n",
"logmodel.summary()"
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
...@@ -605,12 +848,12 @@ ...@@ -605,12 +848,12 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
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\n", 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NHDlygXNuUE3tAjlbprqkOfQTIQbIAk4DEoHPzewL59yqgxZy7gngCYBBgwa5ESNGBLD5n8rOzqa2y4ab7OxsbvrFCJq0Wc9dby8jp6Q1t47uEeqyaiXa3pdo6Eu09APUl6MVSLjnAu2qTGcCm6pps905txfYa2YfA/2AVUhAfnlCB1ZvLWRq9lq6NE/hgiyNIikitRfIMfccoKuZdTSzOOBi4K1D2rwJnGRmMWaWBAwFlge31OhmZtx1Ti9O6NyU219fwoLvdoa6JBGJYDWGu3OuHLgBmIMvsF92zi01s0lmNsnfZjnwLrAY+Ap4yjn3bf2VHZ1ivR4evWwgrdMTuO75BeTt1hk0IlI7AZ3n7pyb7Zzr5pzr7Jy7xz/vMefcY1XaTHHO9XTO9XbOPVhfBUe79KQ4pv1yECVllUx4VmfQiEjt6ArVMNSlRSoPXzqAlVvy+a+XdaNtETl6CvcwNbJ7C/77rON4d+kWHv7P6lCXIyIRRgOHhbFrTuzIii0FPPj+arq3TOXMPq1DXZKIRAjtuYcxM+Oe83ozsH06v3v5G5Zt0m36RCQwCvcwt/8+rGmJsVz73Hx2FJaEuiQRiQAK9wjQIjWBJ67IYnthCb/+59e6i5OI1EjhHiH6ZqZz/wV9+XL9Tv749tJQlyMiYU5fqEaQMQPasnxzPo9/vI6erdO4dGj7UJckImFKe+4R5veje3BKt+bc+da3zN+gIQpEpHoK9wjj9RgPXzyAzGOSmPTCAjZpiAIRqYbCPQKlJcXy5BVZFJdVct3zCyguqwh1SSISZhTuEapLi1T+dlF/luTt4fbXl1DTTVdEpHFRuEew03u25Hend+ONhXlM+3R9qMsRkTCicI9wN4zswuherfjz7OV8snpbqMsRkTChcI9wHo/xwLh+dGmRwg0vLmTjjn2hLklEwoDCPQqkxMfw5BW+++Ve+5zGgBcRhXvUOLZpMv+4dACrtxZwyyvf6AtWkUZO4R5FTuranNvO7MG/v93CIx+uCXU5IhJCCvcoc+1JnRjTvw1/eW8V7y/7IdTliEiIKNyjjJlx3wV96dWmCTfPXMSarYWhLklEQkDhHoUSYr08Pn4Q8TEeJj43nz1FZaEuSUQamMI9SrVNT2Tq5Vls3LmP38xYSIVusi3SqCjco9iQjhnceU4vsldu44G5K0Ndjog0II3nHuUuH9qeZZvymZq9lp6tm3B2vzahLklEGoD23KOcmfHHc3ox6NhjmPzqN3ybtyfUJYlIA1C4NwJxMR6mXp7FMUlxTHxuPtt1k22RqKdwbySap8bz5BWD2LG3lF+9sIDSct1kWySaKdwbkd5t0/i/sX3J2bCLO99aqiEKRKKYvlBtZM7t35blmwt47KO19GydyvhhHUJdkojUA+25N0KTR3Xn1B4t+OPby/hs7fZQlyMi9UDh3gh5PcZDF/enQ7Nkrv/n1xoDXiQKKdwbqdSEWJ66YhCVDiY8l0NBsYYoEIkmCvdGrEOzZB69bCBrt+3l5hmLNESBSBRRuDdyw7s0486ze/LBiq1MmaMhCkSihc6WEcYffywrt/jOoOnWMoXzB2aGuiQRqaOA9tzNbLSZrTSzNWZ22xHaDTazCjMbG7wSpb6ZGXed04thnZpy22tLWPDdzlCXJCJ1VGO4m5kXeAQ4E+gJXGJmPQ/T7n5gTrCLlPoX6/Uw9fKBtElPYOJzC8jdpTNoRCJZIHvuQ4A1zrl1zrlSYAZwbjXtbgReA7YGsT5pQOlJcUy7cjClFZVMeHa+zqARiWBW0yXo/kMso51zE/zT44GhzrkbqrRpC7wInApMA95xzr1azbomAhMBWrZsmTVjxoxaFV1YWEhKSkqtlg034diXpdsr+MuCYno383LzwHg8ZgEtF459qa1o6Uu09APUl/1Gjhy5wDk3qKZ2gXyhWt3/7EM/ER4EbnXOVdgRgsA59wTwBMCgQYPciBEjAtj8T2VnZ1PbZcNNOPZlBJCW+R1/mPUtnxS24M6zewW0XDj2pbaipS/R0g9QX45WIOGeC7SrMp0JbDqkzSBghj/YmwFnmVm5c25WUKqUBnf58ceybttenp63nk7NkjUGjUiECSTcc4CuZtYRyAMuBi6t2sA513H/czObju+wjII9wv2/nx/Hdzv2cudbS8nMSGJk9xahLklEAlTjF6rOuXLgBnxnwSwHXnbOLTWzSWY2qb4LlNDxeoyHLxnAca2bcMM/v2bZpvxQlyQiAQroPHfn3GznXDfnXGfn3D3+eY855x6rpu2V1X2ZKpEpOT6Gab8cTGpCLNc8m8OWPcWhLklEAqDhB6RGrdISmHblIPKLyrh6eg6FJeWhLklEaqBwl4D0apPGI5cNZOUPBdzw4teUV+g2fSLhTOEuARvRvQV/Orc32Su3cceb3+o2fSJhTAOHyVG5dGh7cnft49HstbRNT+SGU7uGuiQRqYbCXY7a5FHd2bKnmAfmrqJVWiJjszSKpEi4UbjLUTMz7rugL1sLSrjttcU0T43nlG7Na7WuWQvzmDJnJZt2F9EmPZHJo7ozZkDbIFcs9UXvX/jSMXeplbgY3yiS3Vqm8qsXFvDN97uPeh2zFuZx++tLyNtdhAPydhdx++tLmLUwL/gFS9Dp/QtvCneptdSEWKZfPZimKXFcPT2HLXuP7gyaKXNWUlRWcdC8orIK3REqQuj9C28Kd6mTFqkJPHvVEBzwwPxifsgP/CKnTbuLjmq+hBe9f+FN4S511ql5CtOvGkxhqeOKaV+xZ19g48C3SU88qvkSXvT+hTeFuwRF38x0bhqYwPrte7n62Rz2ldZ8FevkUd1JjPUeNC8x1svkUd3rq0wJIr1/4U3hLkHTs6mXBy/uz8KNu5j0wteUlh/5GPyYAW259/w+tE1PxIC26Ynce34fnW0RIfT+hTedCilBdVaf1tx3fl9+/9pifjtzEQ9fMgCv5/A3cBkzoK3CIILp/QtfCncJunGD25FfXMbd/1pOcryX+87vi+cIAS8iwadwl3ox4aROFBSX89AHq0mM9XLXOb040i0YRSS4FO5Sb27+WVf2lZbz5CfrSYjzctvoHgp4kQaicJd6Y2b891nHsa+0gsc/Wkd8jJffnd4t1GWJNAoKd6lXZsafzu1NaXklD3+wmliPceNpGklSpL4p3KXeeTy+gcYqKh1/eW8VMV4PvxrROdRliUQ1hbs0CK/HmHJhP8orHfe/uwKH49cjuoS6LJGopXCXBuP1GH8d1w8z+L93V+IcXD9SAS9SHxTu0qBivB7+Oq4/hm9UwfIKx02nddFZNCJBpnCXBuf1GH8Z1x+vx8Pf3l9FSXkFk0d1V8CLBJHCXULC6zGmjO1LXIyHR7PXUlxWyR2/OE4BLxIkCncJGY/H+PN5vYmP8fD0vPXsKy3nnvP6HHEsGhEJjMJdQsrMuPPsnqQmxPD3/6yhsKScv47rT1yMBiwVqQuFu4ScmfFfZ3QnJT6Ge/+9goLicqZePpCkOP3zFKkt7R5J2LjulM7cd34fPlm9jUuf/JJde0tDXZJIxFK4S1i5eEh7Hr0si2Wb87nw8c/J0/04RWpF4S5hZ3TvVjx71RB+yC/mvEfmsWxTfqhLEok4CncJS8M6N+WVScPwmDHu8c/5ZPW2UJckElEU7hK2erRqwhvXn0DmMYlc9UwOM3M2hrokkYihcJew1jotkVcmDWNY56bc+toS7n93BZWVLtRliYQ9hbuEvdSEWJ6+cjCXDGnP1Oy1/OqfC9hXWh7qskTCWkDhbmajzWylma0xs9uqef0yM1vs//nMzPoFv1RpzGK9Hv58Xm/u+EVP3lv2A2Onfs4mnUkjclg1hruZeYFHgDOBnsAlZtbzkGbrgVOcc32BPwFPBLtQETPjmhM7Mu3KwXy/cx9n//1Tvlq/M9RliYSlQPbchwBrnHPrnHOlwAzg3KoNnHOfOed2+Se/ADKDW6bIj0Z2b8Eb1w8nLTGWS5/8guc/34BzOg4vUpXV9J/CzMYCo51zE/zT44GhzrkbDtP+FqDH/vaHvDYRmAjQsmXLrBkzZtSq6MLCQlJSUmq1bLhRX2pvX5nj8cUlfLOtguFtYriiVxzx3uAMOhYt70u09APUl/1Gjhy5wDk3qMaGzrkj/gAXAk9VmR4P/P0wbUcCy4GmNa03KyvL1daHH35Y62XDjfpSNxUVle5v7610HW57x43620du/bbCoKw3Wt6XaOmHc+rLfsB8V0O+OucCOiyTC7SrMp0JbDq0kZn1BZ4CznXO7QhgvSJ15vEYN/+sG89cOZjNe4o5+++f8q/Fm0NdlkjIBRLuOUBXM+toZnHAxcBbVRuYWXvgdWC8c25V8MsUObIR3Vvwr5tOpHOLFK5/8WvumPUtxWUVoS5LJGRqDHfnXDlwAzAH3yGXl51zS81skplN8jf7H6Ap8KiZLTKz+fVWschhZB6TxMvXDePakzry/BffMeaReaz+oSDUZYmEREADZjvnZgOzD5n3WJXnE4CffIEq0tBmL9nM7CVbAFj1QwFnPfwJY/q3Zd6a7WzeU0yb9EQmj+rOmAFtg77tWQvzmDJnJZt2F9XrdgLxh1lLeOnL77m5dxnX3D6bS4a24+4xfUJSi4SG7oYgUWPWwjxuf30JRf7DMZUOXIXjlQW5B9rk7S7i9teXAAQ1eA/ddn1tJxB/mLWEF774cRyeCucOTCvgGw8NPyBRY8qclQfCdb/qTvQtKqtgypyV9b7t+thOIF768vujmi/RSeEuUeNohiMI9k1ADrftUAyRUHGYa1cON1+ik8Jdokab9MSA23oM3lyUF7QrWw+37aOpKVi8Vv2FXIebL9FJ4S5RY/Ko7iTGeg+aF+sxYg+5ajU+xkPmMUn8ZsYiJjw7n9xd++pl24mxXiaP6l7ndR+tS4a2O6r5Ep0U7hI1xgxoy73n96FteiIGtE1PZMqF/Zgytt9B8+6/oC8f3jKCP/z8OD5bu4PT//oxT3y8lrKKyqBu+97z+4TkbJm7x/Th8uPbH9hT95px+fHt9WVqI6OzZSSqjBnQttpArW7ehJM6Mbp3K+56ayl/nr2C1xbk8cdze3F8p6ZB3XYo3D2mD3eP6UN2djZrLxsR6nIkBLTnLo1a5jFJPPXLwTwxPovCknIufuILbnppITuLa78XLxIOtOcuApzRqxUndW3O1I/W8thHa3m3spLvvKu47pROJMXpv4lEHu25i/glxnn53end+OB3p9C/hZeHPljNyAeymZmzkQrdt1UijMJd5BDtMpL4df8EXpk0jNZpidz62hLOfOhj3lv2g24KIhFD4S5yGIM7ZPDGr0/g0csGUlbhuPa5+Yx59DM+Xb1dIS9hT+EucgRmxll9WvPeb0/m/gv6sC2/mMunfcm4xz9n3hqFvIQvhbtIAGK8Hi4a3J7/3DKC/z23F9/vLOKyp77kgqmf8Z8VOlwj4UfhLnIUEmK9XDGsA9mTR/Cnc3vxQ34JV0+fz1kPf8obC3PrdCGUSDAp3EVqISHWy3h/yD9wYT/KKir57cxvOPn/PuTxj9ayZ19ZqEuURk4n8IrUQazXw9isTM4f0JbsVVt54uN13PvvFTz4/mouyGrLFcM60K1laqjLlEZI4S4SBB6PcWqPlpzaoyVLN+1h+rwNvDw/lxe+2MjQjhlcfvyxnNGrJfEx3ppXJhIECneRIOvVJo0pF/bj9rOO4+X53/PCF99x40sLyUiO44KBbRk3qB1dtTcv9UzhLlJPMpLjmHRKZyae1IlP12znpa828sy8DTz5yXr6ZaYxNiuTn/dtQ0ZyXKhLlSikcBepZx6PcXK35pzcrTnbC0uYtTCPVxfkcsebS/nj28sY0b05Z/drw8+Oa0lyvP5LSnDoX5JIA2qWEs+Ekzox4aROLN+cz6yFeby5aBPvL99KQqyHU3u04MzerRnZowUpCnqpA/3rEQmR41o34bjWTbh1dA/mf7eLt7/ZxL+/3cLsJVuIi/FwUpdmnN6zJacd15LmqfGhLlcijMJdJMQ8HmNIxwyGdMzgrnN6seC7Xcxespn3lv3AByu2YraEvpnpnNajBSO6N6d3mzQ8Ht0PVY5M4S4SRrxVgv7Os3uyfHMBHyz3hfzf3l/FX99bRUZyHCd2acaJXZtxYpc9/m12AAANA0lEQVRmIbkJt4Q/hbtImDIzerZpQs82TbjxtK5sLyzh09XbyV65lU/XbOetbzYB0LFZMsd3yuD4Tk0Z0jGD1mkKe1G4i0SMZinxB+7T6pxj5Q8FfLp6O5+v3cE732zmpa++B6BdRiKDj81g4LHH4Aoqqah0eHUYp9FRuItEIDOjR6sm9GjVhAkndaK8opLlmwv4asNOvlq/g49Xb+P1hXkA3Jczh76Z6fRrl06/zDT6tkunTVoCZgr8aKZwF4kCMV4PfTLT6JOZxjUndsQ5x8ad+3jh3c8oSWnNwo27mfbpOsoqfEMTH5MUS++2afRs7Tvsc1zrJnRslkysV2MJRguFu0gUMjOObZrM8LaxjBjRG4CS8gpWbC5gce5ulm7K59tNe3hm3gZK/cMUx3k9dGqeTPdWqXRtkULXlql0aZFC+4wkhX4EUriLNBLxMV7foZl26QfmlVVUsm7bXpZvzmfFlgJWbMln/oZdvLlo04E2sV6jfUYSHZul0Kl5Msc2TaJj02TaN02idVqijueHKYW7SCMW6/XQvVUq3VsdPJBZYUk5a7YWsnZrIWu2FbJ+217Wb9/Lx6u3UVpeWWV5o216Iu0yksg8JonMYxJpm55I22MSaZOeSMvUeGK01x8SCncR+YmU+Bj6t0unf5W9fIDKSseW/GI2bN/Lhh37+H7XPjbu2Efurn3M3bSFHXtLD2rvMWiRmkCrtARaNfE9tmgST4vUBFqkxtOiSTzNUuLJSIrThVlBpnAXkYB5PEabdN9e+Qldfvr6vtJyNu0uIndXEZv3FLN5dxGb9hTzQ34xa7YVMm/tdgqKy3+ynNdjZCTH0TQ5jmYp8TRNiSMjOY6MpDgyUuI4JimO73ZU0HJzPsckxZGWGEtCrEdn/ByBwl1EgiYpLoYuLVLp0uLw49XvKy1na34JWwtK2FpQzPaCErYXlrKtoIQde0vZXljCxp372LW3lIKSgz8I7s/55MDzOK+HtKRYmiTEkJYYS5PEWJokxJKaEEPqgccYUuJ//En2//iee0mKi4na7wwCCnczGw08BHiBp5xz9x3yuvlfPwvYB1zpnPs6yLWKRK1ZC/OYMmclm3YX0SY9kcmjuvPK/I3MW7vzQJvhnTO4cFD7n7QDfjJv/nc7eenL77m5dxnX3D6bS4a24+4xfQLa7pgBbQ87P5Dl92+7wjm8Zj/ZdlJcDB2axbDo+9019uXOs3tycrfm7NxXyofzcujQrSe79pXx2drtZK/cxraCEgqKy/B6jLIKx4bte8kvLqeguOzAaZ81iY/xkBTnC/qkOC+JcV4SY70kxXlJiPU9T4jzkhDjJSHWQ0Lsj4/xMR7iY/yPsT8+j9v/4/3xebzXS2yMEev14FxgtdVFjeFuZl7gEeB0IBfIMbO3nHPLqjQ7E+jq/xkKTPU/ikgNZi3M4/bXl1BUVgFA3u4ibp656Cft5q3deVDY5+0uYvKr34CDskp3YN7vZi6isspyFc7xwhcbAQ4K2eq2e/vrS5j/3U5eW5D3k/nAQQFf3fJ12fbkV74B40Ao5+0u4o43l3Lv+X0YM6AtW5p6GdGnNbMW5vHB8q0Hli0uq+T7nUUH2u1XXFZBYUk5hcXlFBSXU1hSzt6ScvaWlrO3pIK9JeXsK61gb2k5+0p9z4tKKw487thbemC6pLyC4rJKisoqqKisezCf2TGWkSPrvJojCmTPfQiwxjm3DsDMZgDnAlXD/VzgOef7OPrCzNLNrLVzbnPQKxaJMlPmrDwQVEerur3TymraAbz05fcHBWx12y0qqziw133o/ClzVh4UntUtX5dtl1UTmoFut7p2vj1sL81SgjtccllFJcVlFZSUV1JS7nte6n/ue6ygpKyS0grf9IHH8krKKnw/nl0bg1pTdaymPw/MbCww2jk3wT89HhjqnLuhSpt3gPucc5/6pz8AbnXOzT9kXROBif7J7sDKWtbdDNhey2XDjfoSnhqsL3GtumTV17or9u3Bm5R2YLp0y5oFddluXZYPwrLNgO1HWrbqNsJcXf59Heuca15To0D23Kv7tuHQT4RA2uCcewJ4IoBtHrkgs/nOuUF1XU84UF/CU7T0xczml+/ZGvH9gOh5T6Bh+hLI1QW5QLsq05nAplq0ERGRBhJIuOcAXc2so5nFARcDbx3S5i3gCvM5Htij4+0iIqFT42EZ51y5md0AzMF3KuTTzrmlZjbJ//pjwGx8p0GuwXcq5FX1VzIQhEM7YUR9CU/R0pdo6QeoL0elxi9URUQk8mhEHxGRKKRwFxGJQmEf7maWYGZfmdk3ZrbUzP7on59hZu+Z2Wr/4zGhrjUQZuY1s4X+awMiuR8bzGyJmS0ys/n+eZHal3Qze9XMVpjZcjMbFol9MbPu/vdj/0++md0coX35rf//+7dm9pI/ByKuHwBm9ht/P5aa2c3+efXel7APd6AEONU51w/oD4z2n5FzG/CBc64r8IF/OhL8BlheZTpS+wEw0jnXv8r5upHal4eAd51zPYB++N6fiOuLc26l//3oD2ThO7nhDSKsL2bWFrgJGOSc643vRI6LibB+AJhZb+BafFf69wN+YWZdaYi+OOci5gdIAr7GN27NSqC1f35rYGWo6wug/kz/G3kq8I5/XsT1w1/rBqDZIfMiri9AE2A9/pMLIrkvh9R/BjAvEvsCtAW+BzLwndH3jr8/EdUPf50X4htscf/0HcDvG6IvkbDnvv9QxiJgK/Cec+5LoKXzn0vvf2wRyhoD9CC+N7bqEByR2A/wXYE818wW+IeVgMjsSydgG/CM/3DZU2aWTGT2paqLgZf8zyOqL865POABYCOwGd91M3OJsH74fQucbGZNzSwJ3ynj7WiAvkREuDvnKpzvT81MYIj/T52IYma/ALY65yJl7IuaDHfODcQ3Iuj1ZnZyqAuqpRhgIDDVOTcA2EsE/Ll/JP6LDc8BXgl1LbXhP/58LtARaAMkm9nloa2qdpxzy4H7gfeAd4FvgJ/eraQeRES47+ec2w1kA6OBH8ysNYD/cWsISwvEcOAcM9sAzABONbMXiLx+AOCc2+R/3IrvuO4QIrMvuUCu/69BgFfxhX0k9mW/M4GvnXM/+KcjrS8/A9Y757Y558qA14ETiLx+AOCcm+acG+icOxnYCaymAfoS9uFuZs3NLN3/PBHfG78C35AHv/Q3+yXwZmgqDIxz7nbnXKZzrgO+P5n/45y7nAjrB4CZJZtZ6v7n+I6HfksE9sU5twX43sy6+2edhm8464jrSxWX8OMhGYi8vmwEjjezJDMzfO/JciKvHwCYWQv/Y3vgfHzvTb33JeyvUDWzvsCz+L4x9wAvO+f+18yaAi8D7fH9Y7jQObfz8GsKH2Y2ArjFOfeLSOyHmXXCt7cOvsMaLzrn7onEvgCYWX/gKSAOWIdv+AwPkdmXJHxfRnZyzu3xz4u498V/yvNF+A5hLAQmAClEWD8AzOwToClQBvzOOfdBQ7wnYR/uIiJy9ML+sIyIiBw9hbuISBRSuIuIRCGFu4hIFFK4i4hEoUBukC3SoPyniX3gn2wFVOAbIgBgiHOuNCSFHYGZXQ3M9p83LxJyOhVSwpqZ3QUUOuceCINavM65isO89ilwg3Nu0VGsL8Y51yCXokvjo8MyElHM7JfmG99/kZk9amYeM4sxs91mNsXMvjazOWY21Mw+MrN1ZnaWf9kJZvaG//WVZvaHANd7t5l9hW9coz+aWY5/fO7HzOcifMNRz/QvH2dmuVWurD7ezN73P7/bzB43s/fwDVYWY2Z/9W97sZlNaPjfqkQjhbtEDP+AcecBJ/gHkovBN5QDQBow1z+YWSlwF77L1i8E/rfKaob4lxkIXGpm/QNY79fOuSHOuc+Bh5xzg4E+/tdGO+dmAouAi5xvPPWaDhsNAM52zo0HJuIbUG4IMBjfIGzta/P7EalKx9wlkvwMXwDO9w05QiK+S+0Bipxz7/mfL8E3TGy5mS0BOlRZxxzn3C4AM5sFnIjv/8Hh1lvKj0MtAJxmZpOBBKAZsAD491H2403nXLH/+RnAcWZW9cOkK75L0kVqTeEukcSAp51zdxw00ywGXwjvV4nvDl77n1f9d37ol0yuhvUWOf8XU/5xW/4BDHTO5ZnZ3fhCvjrl/PiX8aFt9h7Sp1875z5AJIh0WEYiyfvAODNrBr6zampxCOMM890zNQnfmOHzjmK9ifg+LLb7R8W8oMprBUBqlekN+G51xyHtDjUH+LX/g2T/fVATj7JPIj+hPXeJGM65Jf7RAt83Mw++UfYmAZuOYjWfAi8CnYHn95/dEsh6nXM7zOxZfMMbfwd8WeXlZ4CnzKwI33H9u4AnzWwL8NUR6nkc38iAi/yHhLbi+9ARqROdCimNhv9MlN7OuZtDXYtIfdNhGRGRKKQ9dxGRKKQ9dxGRKKRwFxGJQgp3EZEopHAXEYlCCncRkSj0/wHRUJwHFwSFegAAAABJRU5ErkJggg==\n",
"text/plain": [ "text/plain": [
"<Figure size 432x288 with 1 Axes>" "<Figure size 432x288 with 1 Axes>"
] ]
...@@ -648,7 +891,34 @@ ...@@ -648,7 +891,34 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"data_pred = pd.DataFrame({'Pressure': np.linspace(start=0, stop=250, num=121), 'Intercept': 1})\n",
"data_pred['Frequency'] = logmodel.predict(data_pred[['Intercept','Pressure']])\n",
"data_pred.plot(x=\"Pressure\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n",
"plt.scatter(x=data[\"Pressure\"],y=data[\"Frequency\"])\n",
"plt.grid(True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -705,7 +975,7 @@ ...@@ -705,7 +975,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.7.3" "version": "3.6.4"
} }
}, },
"nbformat": 4, "nbformat": 4,
......
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