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wroscoe/gist:38336eaaf4af46617f494263b65144eb

Created September 22, 2017 04:55
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gistfile1.txt
{
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"import donkeycar as dk\n",
"\n",
"from PIL import Image\n",
"import os\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tub does exist\n"
]
},
{
"data": {
"text/plain": [
"{'cam/image_array': '3_cam-image_array_.jpg',\n",
" 'user/angle': -0.141705766475975,\n",
" 'user/mode': 'user',\n",
" 'user/throttle': 0.5069282409952348}"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tub_path = '/home/wroscoe/d2/data/diy_1/'\n",
"T = dk.parts.Tub(path=tub_path)\n",
"T.get_json_record(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Shift records forward."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.DataFrame([T.get_json_record(i) for i in T.get_index(shuffled=False)])\n",
"#df['user/angle'] = df['user/angle'].shift(-10)\n",
"df = df.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import random"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [],
"source": [
"index = list(df.index)\n",
"random.shuffle(index)"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_record_from_df(df, data_path, record_transform=None, shuffle=False):\n",
" \n",
" index = list(df.index)\n",
" if shuffle:\n",
" random.shuffle(index)\n",
" while True:\n",
" for i in index:\n",
" row = dict(df.ix[i])\n",
" img = Image.open(os.path.join(data_path, row['cam/image_array']))\n",
" row['cam/image_array'] = np.array(img)\n",
" if record_transform:\n",
" row = record_transform(row)\n",
" yield row\n",
"\n",
" \n",
"def get_batch_from_df(keys, df, record_transform=None, batch_size=128,\n",
" record_tranform=None, data_path=None, shuffle=False):\n",
" \n",
" record_gen = get_record_from_df(df, data_path, record_transform, shuffle=shuffle)\n",
" \n",
" if keys==None:\n",
" keys = list(df.columns)\n",
" while True:\n",
" record_list = []\n",
" for _ in range(batch_size):\n",
" record_list.append(next(record_gen))\n",
"\n",
" batch_arrays = {}\n",
" for i, k in enumerate(keys):\n",
" arr = np.array([r[k] for r in record_list])\n",
" #if len(arr.shape) == 1:\n",
" # arr = arr.reshape(arr.shape + (1,))\n",
" batch_arrays[k] = arr\n",
"\n",
" yield batch_arrays\n",
" \n",
"def train_gen(X_keys, Y_keys, df, batch_size=128, data_path=None, \n",
" record_transform=None):\n",
" \n",
" batch_gen = get_batch_from_df(X_keys+Y_keys, df, batch_size=128, record_transform=None, \n",
" data_path=data_path)\n",
" while True:\n",
" batch = next(batch_gen)\n",
" X = [batch[k] for k in X_keys]\n",
" Y = [batch[k] for k in Y_keys]\n",
" yield X,Y\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [],
"source": [
"cutoff = int(len(df)*.8//1)"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [],
"source": [
"tt = T.train_gen(['cam/image_array'], ['user/angle', 'user/throttle'])\n"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [],
"source": [
"t = train_gen(['cam/image_array'], ['user/angle', 'user/throttle'], df[:cutoff], batch_size=128, data_path=T.path)\n",
"v = train_gen(['cam/image_array'], ['user/angle', 'user/throttle'], df[cutoff:], batch_size=128, data_path=T.path)"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"m = dk.parts.ml.keras.default_linear()"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"100/100 [==============================] - 157s - loss: 900.3821 - angle_out_loss: 1000.3857 - throttle_out_loss: 35.0489 - val_loss: 0.0359 - val_angle_out_loss: 0.0395 - val_throttle_out_loss: 0.3626\n",
"Epoch 2/10\n",
"100/100 [==============================] - 177s - loss: 0.0408 - angle_out_loss: 0.0451 - throttle_out_loss: 0.2347 - val_loss: 0.0261 - val_angle_out_loss: 0.0287 - val_throttle_out_loss: 0.1993\n",
"Epoch 3/10\n",
"100/100 [==============================] - 174s - loss: 0.0403 - angle_out_loss: 0.0446 - throttle_out_loss: 0.1099 - val_loss: 0.0576 - val_angle_out_loss: 0.0639 - val_throttle_out_loss: 0.0614\n",
"Epoch 4/10\n",
"100/100 [==============================] - 170s - loss: 0.0327 - angle_out_loss: 0.0362 - throttle_out_loss: 0.0763 - val_loss: 0.0384 - val_angle_out_loss: 0.0426 - val_throttle_out_loss: 0.0246\n",
"Epoch 5/10\n",
"100/100 [==============================] - 159s - loss: 0.0177 - angle_out_loss: 0.0197 - throttle_out_loss: 0.0173 - val_loss: 0.0155 - val_angle_out_loss: 0.0172 - val_throttle_out_loss: 0.0154\n",
"Epoch 6/10\n",
"100/100 [==============================] - 172s - loss: 0.0108 - angle_out_loss: 0.0120 - throttle_out_loss: 0.0124 - val_loss: 0.0265 - val_angle_out_loss: 0.0294 - val_throttle_out_loss: 0.0107\n",
"Epoch 7/10\n",
"100/100 [==============================] - 181s - loss: 0.0079 - angle_out_loss: 0.0088 - throttle_out_loss: 0.0096 - val_loss: 0.0132 - val_angle_out_loss: 0.0146 - val_throttle_out_loss: 0.0112\n",
"Epoch 8/10\n",
"100/100 [==============================] - 179s - loss: 0.0064 - angle_out_loss: 0.0071 - throttle_out_loss: 0.0085 - val_loss: 0.0232 - val_angle_out_loss: 0.0257 - val_throttle_out_loss: 0.0080\n",
"Epoch 9/10\n",
"100/100 [==============================] - 174s - loss: 0.0057 - angle_out_loss: 0.0063 - throttle_out_loss: 0.0080 - val_loss: 0.0149 - val_angle_out_loss: 0.0166 - val_throttle_out_loss: 0.0076\n",
"Epoch 10/10\n",
"100/100 [==============================] - 177s - loss: 0.0046 - angle_out_loss: 0.0051 - throttle_out_loss: 0.0075 - val_loss: 0.0178 - val_angle_out_loss: 0.0197 - val_throttle_out_loss: 0.0086\n"
]
}
],
"source": [
"hist = m.fit_generator(\n",
" t, \n",
" steps_per_epoch=100, \n",
" epochs=10, \n",
" verbose=1, \n",
" validation_data=v,\n",
" validation_steps=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [],
"source": [
"img_gen = get_batch_from_df(['cam/image_array'], df, batch_size=len(df), data_path=tub_path)\n",
"img = next(img_gen)"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(5652, 120, 160, 3)"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img['cam/image_array'].shape"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {},
"outputs": [],
"source": [
"predictions = m.predict(img['cam/image_array'])"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {},
"outputs": [],
"source": [
"df['pilot/angle'] = predictions[0]"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff9851c6208>"
]
},
"execution_count": 129,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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nwZVLMp4TtemRG+2Gj7bcqUH7Tj3qAlKWv1exxH+wEo6rVIog7TUz4ZJnMx/M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\nszYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff9851ab208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df[['user/angle', 'pilot/angle']][:500].plot()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"m = keras.models.load_model('/home/wroscoe/d2/models/diy_lin_1250')"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([[-0.04507893],\n",
" [-0.00287324],\n",
" [-0.06904186],\n",
" [-0.00423036],\n",
" [-0.00325876],\n",
" [-0.02970172],\n",
" [-0.02447423],\n",
" [-0.02693687],\n",
" [-0.02493166],\n",
" [-0.03716014],\n",
" [-0.00582808],\n",
" [-0.08257235],\n",
" [-0.05419559],\n",
" [-0.01814637],\n",
" [-0.05955185],\n",
" [-0.03689044],\n",
" [-0.05757111],\n",
" [-0.05949275],\n",
" [ 0.01716883],\n",
" [-0.03822539],\n",
" [-0.14090078],\n",
" [-0.04362478],\n",
" [ 0.02009978],\n",
" [-0.18130453],\n",
" [-0.40281731],\n",
" [-0.41715845],\n",
" [-0.38310999],\n",
" [-0.2565136 ],\n",
" [-0.14606544],\n",
" [-0.34517416],\n",
" [-0.20033343],\n",
" [-0.10754998],\n",
" [-0.00058938],\n",
" [-0.06935023],\n",
" [-0.1407312 ],\n",
" [-0.15278848],\n",
" [-0.22463991],\n",
" [-0.2109777 ],\n",
" [-0.17666306],\n",
" [-0.33965462],\n",
" [-0.26438865],\n",
" [-0.29222444],\n",
" [-0.30108306],\n",
" [-0.2601181 ],\n",
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]
},
"execution_count": 144,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result = m.predict(r[0])\n",
"#np.argmax(result[0], axis=1)\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {},
"outputs": [
{
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" <td>0.002806</td>\n",
" <td>0.013395</td>\n",
" <td>0.048595</td>\n",
" <td>0.729796</td>\n",
" <td>0.112151</td>\n",
" <td>0.041433</td>\n",
" <td>0.016698</td>\n",
" <td>0.018658</td>\n",
" <td>0.004329</td>\n",
" <td>0.006065</td>\n",
" <td>0.002508</td>\n",
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" <td>0.007143</td>\n",
" <td>0.070275</td>\n",
" <td>0.115415</td>\n",
" <td>0.524635</td>\n",
" <td>0.143762</td>\n",
" <td>0.055408</td>\n",
" <td>0.032890</td>\n",
" <td>0.030774</td>\n",
" <td>0.004361</td>\n",
" <td>0.007500</td>\n",
" <td>0.002571</td>\n",
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" <tr>\n",
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" <td>0.523270</td>\n",
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" <td>0.028459</td>\n",
" <td>0.029239</td>\n",
" <td>0.034106</td>\n",
" <td>0.002812</td>\n",
" <td>0.005876</td>\n",
" <td>0.002918</td>\n",
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" <tr>\n",
" <th>13</th>\n",
" <td>0.000581</td>\n",
" <td>0.000669</td>\n",
" <td>0.000410</td>\n",
" <td>0.000672</td>\n",
" <td>0.002426</td>\n",
" <td>0.013207</td>\n",
" <td>0.026962</td>\n",
" <td>0.505740</td>\n",
" <td>0.334069</td>\n",
" <td>0.050117</td>\n",
" <td>0.022814</td>\n",
" <td>0.036167</td>\n",
" <td>0.002289</td>\n",
" <td>0.002321</td>\n",
" <td>0.001555</td>\n",
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" <tr>\n",
" <th>14</th>\n",
" <td>0.000584</td>\n",
" <td>0.001342</td>\n",
" <td>0.001889</td>\n",
" <td>0.001014</td>\n",
" <td>0.008103</td>\n",
" <td>0.016353</td>\n",
" <td>0.032403</td>\n",
" <td>0.596749</td>\n",
" <td>0.204039</td>\n",
" <td>0.079260</td>\n",
" <td>0.030771</td>\n",
" <td>0.021365</td>\n",
" <td>0.002941</td>\n",
" <td>0.002345</td>\n",
" <td>0.000843</td>\n",
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" <tr>\n",
" <th>15</th>\n",
" <td>0.001876</td>\n",
" <td>0.000791</td>\n",
" <td>0.000661</td>\n",
" <td>0.000530</td>\n",
" <td>0.015188</td>\n",
" <td>0.031775</td>\n",
" <td>0.109170</td>\n",
" <td>0.534323</td>\n",
" <td>0.256911</td>\n",
" <td>0.025053</td>\n",
" <td>0.010340</td>\n",
" <td>0.007189</td>\n",
" <td>0.001494</td>\n",
" <td>0.003045</td>\n",
" <td>0.001653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0.002000</td>\n",
" <td>0.000724</td>\n",
" <td>0.001064</td>\n",
" <td>0.001519</td>\n",
" <td>0.011146</td>\n",
" <td>0.011766</td>\n",
" <td>0.021432</td>\n",
" <td>0.450818</td>\n",
" <td>0.428953</td>\n",
" <td>0.043911</td>\n",
" <td>0.015116</td>\n",
" <td>0.007023</td>\n",
" <td>0.000915</td>\n",
" <td>0.001940</td>\n",
" <td>0.001673</td>\n",
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" <tr>\n",
" <th>17</th>\n",
" <td>0.006862</td>\n",
" <td>0.001878</td>\n",
" <td>0.002407</td>\n",
" <td>0.002745</td>\n",
" <td>0.019984</td>\n",
" <td>0.051714</td>\n",
" <td>0.095929</td>\n",
" <td>0.358381</td>\n",
" <td>0.369931</td>\n",
" <td>0.036588</td>\n",
" <td>0.017278</td>\n",
" <td>0.019750</td>\n",
" <td>0.006067</td>\n",
" <td>0.003543</td>\n",
" <td>0.006943</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.003598</td>\n",
" <td>0.001317</td>\n",
" <td>0.004068</td>\n",
" <td>0.004550</td>\n",
" <td>0.007880</td>\n",
" <td>0.021334</td>\n",
" <td>0.044755</td>\n",
" <td>0.396489</td>\n",
" <td>0.402196</td>\n",
" <td>0.059507</td>\n",
" <td>0.018962</td>\n",
" <td>0.025530</td>\n",
" <td>0.002319</td>\n",
" <td>0.003132</td>\n",
" <td>0.004364</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0.001463</td>\n",
" <td>0.001585</td>\n",
" <td>0.010295</td>\n",
" <td>0.007534</td>\n",
" <td>0.026094</td>\n",
" <td>0.029908</td>\n",
" <td>0.076007</td>\n",
" <td>0.471224</td>\n",
" <td>0.221922</td>\n",
" <td>0.059176</td>\n",
" <td>0.052063</td>\n",
" <td>0.031353</td>\n",
" <td>0.006133</td>\n",
" <td>0.003190</td>\n",
" <td>0.002053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.003687</td>\n",
" <td>0.002116</td>\n",
" <td>0.008042</td>\n",
" <td>0.009403</td>\n",
" <td>0.036685</td>\n",
" <td>0.054722</td>\n",
" <td>0.103267</td>\n",
" <td>0.461837</td>\n",
" <td>0.191535</td>\n",
" <td>0.042185</td>\n",
" <td>0.054491</td>\n",
" <td>0.020491</td>\n",
" <td>0.006196</td>\n",
" <td>0.003343</td>\n",
" <td>0.001999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0.000684</td>\n",
" <td>0.000297</td>\n",
" <td>0.000536</td>\n",
" <td>0.001476</td>\n",
" <td>0.015776</td>\n",
" <td>0.042066</td>\n",
" <td>0.034512</td>\n",
" <td>0.720035</td>\n",
" <td>0.140629</td>\n",
" <td>0.008838</td>\n",
" <td>0.028773</td>\n",
" <td>0.003289</td>\n",
" <td>0.001162</td>\n",
" <td>0.001582</td>\n",
" <td>0.000345</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0.002152</td>\n",
" <td>0.000516</td>\n",
" <td>0.000668</td>\n",
" <td>0.001525</td>\n",
" <td>0.009948</td>\n",
" <td>0.029619</td>\n",
" <td>0.029172</td>\n",
" <td>0.604726</td>\n",
" <td>0.265445</td>\n",
" <td>0.016037</td>\n",
" <td>0.028722</td>\n",
" <td>0.005465</td>\n",
" <td>0.001608</td>\n",
" <td>0.003275</td>\n",
" <td>0.001120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.002113</td>\n",
" <td>0.000095</td>\n",
" <td>0.000114</td>\n",
" <td>0.000500</td>\n",
" <td>0.001584</td>\n",
" <td>0.016861</td>\n",
" <td>0.011649</td>\n",
" <td>0.623052</td>\n",
" <td>0.303452</td>\n",
" <td>0.025280</td>\n",
" <td>0.006112</td>\n",
" <td>0.007360</td>\n",
" <td>0.000370</td>\n",
" <td>0.001037</td>\n",
" <td>0.000420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0.007268</td>\n",
" <td>0.000238</td>\n",
" <td>0.000130</td>\n",
" <td>0.000781</td>\n",
" <td>0.001389</td>\n",
" <td>0.016094</td>\n",
" <td>0.016393</td>\n",
" <td>0.478390</td>\n",
" <td>0.411849</td>\n",
" <td>0.043488</td>\n",
" <td>0.008673</td>\n",
" <td>0.012021</td>\n",
" <td>0.000471</td>\n",
" <td>0.001505</td>\n",
" <td>0.001311</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0.000597</td>\n",
" <td>0.000105</td>\n",
" <td>0.000093</td>\n",
" <td>0.000340</td>\n",
" <td>0.000879</td>\n",
" <td>0.011234</td>\n",
" <td>0.010297</td>\n",
" <td>0.647319</td>\n",
" <td>0.267520</td>\n",
" <td>0.040340</td>\n",
" <td>0.009551</td>\n",
" <td>0.009212</td>\n",
" <td>0.000427</td>\n",
" <td>0.001581</td>\n",
" <td>0.000504</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0.000772</td>\n",
" <td>0.000134</td>\n",
" <td>0.000586</td>\n",
" <td>0.001547</td>\n",
" <td>0.005980</td>\n",
" <td>0.027154</td>\n",
" <td>0.055915</td>\n",
" <td>0.701369</td>\n",
" <td>0.159252</td>\n",
" <td>0.019929</td>\n",
" <td>0.009854</td>\n",
" <td>0.014688</td>\n",
" <td>0.001070</td>\n",
" <td>0.001044</td>\n",
" <td>0.000707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0.000468</td>\n",
" <td>0.000041</td>\n",
" <td>0.000333</td>\n",
" <td>0.001973</td>\n",
" <td>0.004937</td>\n",
" <td>0.048043</td>\n",
" <td>0.051649</td>\n",
" <td>0.789747</td>\n",
" <td>0.079604</td>\n",
" <td>0.007369</td>\n",
" <td>0.005465</td>\n",
" <td>0.009175</td>\n",
" <td>0.000372</td>\n",
" <td>0.000663</td>\n",
" <td>0.000159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.000305</td>\n",
" <td>0.000135</td>\n",
" <td>0.000257</td>\n",
" <td>0.000235</td>\n",
" <td>0.001835</td>\n",
" <td>0.005513</td>\n",
" <td>0.007199</td>\n",
" <td>0.571161</td>\n",
" <td>0.365943</td>\n",
" <td>0.030701</td>\n",
" <td>0.008764</td>\n",
" <td>0.006503</td>\n",
" <td>0.000545</td>\n",
" <td>0.000572</td>\n",
" <td>0.000331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0.000302</td>\n",
" <td>0.000098</td>\n",
" <td>0.000135</td>\n",
" <td>0.000298</td>\n",
" <td>0.000787</td>\n",
" <td>0.004093</td>\n",
" <td>0.005622</td>\n",
" <td>0.583510</td>\n",
" <td>0.347018</td>\n",
" <td>0.037687</td>\n",
" <td>0.010870</td>\n",
" <td>0.008513</td>\n",
" <td>0.000368</td>\n",
" <td>0.000490</td>\n",
" <td>0.000210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>0.005951</td>\n",
" <td>0.005064</td>\n",
" <td>0.004709</td>\n",
" <td>0.012446</td>\n",
" <td>0.038810</td>\n",
" <td>0.292921</td>\n",
" <td>0.202148</td>\n",
" <td>0.306325</td>\n",
" <td>0.027954</td>\n",
" <td>0.020051</td>\n",
" <td>0.046290</td>\n",
" <td>0.020704</td>\n",
" <td>0.005912</td>\n",
" <td>0.008118</td>\n",
" <td>0.002597</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>0.012083</td>\n",
" <td>0.009363</td>\n",
" <td>0.009550</td>\n",
" <td>0.032847</td>\n",
" <td>0.042767</td>\n",
" <td>0.176283</td>\n",
" <td>0.129478</td>\n",
" <td>0.356869</td>\n",
" <td>0.048585</td>\n",
" <td>0.042097</td>\n",
" <td>0.076583</td>\n",
" <td>0.034356</td>\n",
" <td>0.009283</td>\n",
" <td>0.013826</td>\n",
" <td>0.006030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>0.012490</td>\n",
" <td>0.008666</td>\n",
" <td>0.010667</td>\n",
" <td>0.025477</td>\n",
" <td>0.044867</td>\n",
" <td>0.111059</td>\n",
" <td>0.097862</td>\n",
" <td>0.425701</td>\n",
" <td>0.058746</td>\n",
" <td>0.068536</td>\n",
" <td>0.070689</td>\n",
" <td>0.034908</td>\n",
" <td>0.012580</td>\n",
" <td>0.009955</td>\n",
" <td>0.007796</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td>0.017078</td>\n",
" <td>0.005925</td>\n",
" <td>0.008030</td>\n",
" <td>0.020460</td>\n",
" <td>0.037724</td>\n",
" <td>0.120288</td>\n",
" <td>0.147378</td>\n",
" <td>0.341156</td>\n",
" <td>0.096774</td>\n",
" <td>0.056462</td>\n",
" <td>0.065353</td>\n",
" <td>0.047448</td>\n",
" <td>0.014276</td>\n",
" <td>0.010095</td>\n",
" <td>0.011552</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>0.009755</td>\n",
" <td>0.003072</td>\n",
" <td>0.003409</td>\n",
" <td>0.013082</td>\n",
" <td>0.021136</td>\n",
" <td>0.080980</td>\n",
" <td>0.080735</td>\n",
" <td>0.461166</td>\n",
" <td>0.150579</td>\n",
" <td>0.053019</td>\n",
" <td>0.060831</td>\n",
" <td>0.037480</td>\n",
" <td>0.005660</td>\n",
" <td>0.008724</td>\n",
" <td>0.010373</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>0.014257</td>\n",
" <td>0.004840</td>\n",
" <td>0.004938</td>\n",
" <td>0.014935</td>\n",
" <td>0.027716</td>\n",
" <td>0.133843</td>\n",
" <td>0.132579</td>\n",
" <td>0.364001</td>\n",
" <td>0.118931</td>\n",
" <td>0.049530</td>\n",
" <td>0.054856</td>\n",
" <td>0.044964</td>\n",
" <td>0.009591</td>\n",
" <td>0.013625</td>\n",
" <td>0.011395</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>0.003738</td>\n",
" <td>0.001680</td>\n",
" <td>0.002349</td>\n",
" <td>0.010269</td>\n",
" <td>0.033786</td>\n",
" <td>0.215440</td>\n",
" <td>0.188105</td>\n",
" <td>0.401550</td>\n",
" <td>0.054096</td>\n",
" <td>0.021966</td>\n",
" <td>0.031242</td>\n",
" <td>0.021022</td>\n",
" <td>0.007193</td>\n",
" <td>0.005934</td>\n",
" <td>0.001631</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>0.008697</td>\n",
" <td>0.002618</td>\n",
" <td>0.002177</td>\n",
" <td>0.008209</td>\n",
" <td>0.043952</td>\n",
" <td>0.232946</td>\n",
" <td>0.204515</td>\n",
" <td>0.307876</td>\n",
" <td>0.106385</td>\n",
" <td>0.020108</td>\n",
" <td>0.029689</td>\n",
" <td>0.014908</td>\n",
" <td>0.005784</td>\n",
" <td>0.009434</td>\n",
" <td>0.002703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td>0.007266</td>\n",
" <td>0.002043</td>\n",
" <td>0.002538</td>\n",
" <td>0.012946</td>\n",
" <td>0.027433</td>\n",
" <td>0.182838</td>\n",
" <td>0.162091</td>\n",
" <td>0.369198</td>\n",
" <td>0.144422</td>\n",
" <td>0.021074</td>\n",
" <td>0.026757</td>\n",
" <td>0.022389</td>\n",
" <td>0.004991</td>\n",
" <td>0.010541</td>\n",
" <td>0.003473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td>0.007306</td>\n",
" <td>0.002323</td>\n",
" <td>0.002576</td>\n",
" <td>0.007011</td>\n",
" <td>0.037841</td>\n",
" <td>0.193709</td>\n",
" <td>0.174264</td>\n",
" <td>0.368229</td>\n",
" <td>0.129330</td>\n",
" <td>0.025554</td>\n",
" <td>0.021307</td>\n",
" <td>0.017691</td>\n",
" <td>0.005673</td>\n",
" <td>0.005215</td>\n",
" <td>0.001972</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>0.018556</td>\n",
" <td>0.004530</td>\n",
" <td>0.004751</td>\n",
" <td>0.017410</td>\n",
" <td>0.023363</td>\n",
" <td>0.132356</td>\n",
" <td>0.097402</td>\n",
" <td>0.374592</td>\n",
" <td>0.149907</td>\n",
" <td>0.058947</td>\n",
" <td>0.040111</td>\n",
" <td>0.049470</td>\n",
" <td>0.009569</td>\n",
" <td>0.011719</td>\n",
" <td>0.007316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td>0.021885</td>\n",
" <td>0.005318</td>\n",
" <td>0.004059</td>\n",
" <td>0.014414</td>\n",
" <td>0.019601</td>\n",
" <td>0.085293</td>\n",
" <td>0.064110</td>\n",
" <td>0.372145</td>\n",
" <td>0.178855</td>\n",
" <td>0.102141</td>\n",
" <td>0.053135</td>\n",
" <td>0.046302</td>\n",
" <td>0.007855</td>\n",
" <td>0.013709</td>\n",
" <td>0.011179</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>0.014114</td>\n",
" <td>0.012873</td>\n",
" <td>0.010526</td>\n",
" <td>0.017478</td>\n",
" <td>0.069933</td>\n",
" <td>0.212864</td>\n",
" <td>0.171863</td>\n",
" <td>0.235218</td>\n",
" <td>0.079648</td>\n",
" <td>0.051693</td>\n",
" <td>0.049476</td>\n",
" <td>0.027906</td>\n",
" <td>0.016730</td>\n",
" <td>0.021295</td>\n",
" <td>0.008384</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111</th>\n",
" <td>0.009336</td>\n",
" <td>0.007401</td>\n",
" <td>0.009502</td>\n",
" <td>0.018849</td>\n",
" <td>0.056976</td>\n",
" <td>0.183016</td>\n",
" <td>0.187676</td>\n",
" <td>0.274916</td>\n",
" <td>0.110657</td>\n",
" <td>0.037982</td>\n",
" <td>0.040759</td>\n",
" <td>0.029551</td>\n",
" <td>0.011673</td>\n",
" <td>0.016540</td>\n",
" <td>0.005166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>0.007150</td>\n",
" <td>0.004227</td>\n",
" <td>0.005983</td>\n",
" <td>0.015288</td>\n",
" <td>0.045940</td>\n",
" <td>0.296108</td>\n",
" <td>0.203347</td>\n",
" <td>0.252054</td>\n",
" <td>0.070659</td>\n",
" <td>0.022142</td>\n",
" <td>0.023900</td>\n",
" <td>0.024324</td>\n",
" <td>0.010055</td>\n",
" <td>0.015052</td>\n",
" <td>0.003770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>0.009885</td>\n",
" <td>0.002930</td>\n",
" <td>0.002925</td>\n",
" <td>0.012656</td>\n",
" <td>0.020277</td>\n",
" <td>0.163696</td>\n",
" <td>0.099853</td>\n",
" <td>0.397851</td>\n",
" <td>0.118069</td>\n",
" <td>0.061566</td>\n",
" <td>0.041452</td>\n",
" <td>0.046942</td>\n",
" <td>0.005816</td>\n",
" <td>0.010985</td>\n",
" <td>0.005099</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td>0.003168</td>\n",
" <td>0.002068</td>\n",
" <td>0.002920</td>\n",
" <td>0.007215</td>\n",
" <td>0.037575</td>\n",
" <td>0.264716</td>\n",
" <td>0.213504</td>\n",
" <td>0.319680</td>\n",
" <td>0.067740</td>\n",
" <td>0.017075</td>\n",
" <td>0.027577</td>\n",
" <td>0.019909</td>\n",
" <td>0.005508</td>\n",
" <td>0.009567</td>\n",
" <td>0.001779</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td>0.008044</td>\n",
" <td>0.004917</td>\n",
" <td>0.007634</td>\n",
" <td>0.015607</td>\n",
" <td>0.048700</td>\n",
" <td>0.172218</td>\n",
" <td>0.109372</td>\n",
" <td>0.392536</td>\n",
" <td>0.096442</td>\n",
" <td>0.039451</td>\n",
" <td>0.042452</td>\n",
" <td>0.030450</td>\n",
" <td>0.013520</td>\n",
" <td>0.014595</td>\n",
" <td>0.004061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>0.004359</td>\n",
" <td>0.002325</td>\n",
" <td>0.003371</td>\n",
" <td>0.008491</td>\n",
" <td>0.043149</td>\n",
" <td>0.213245</td>\n",
" <td>0.176399</td>\n",
" <td>0.400512</td>\n",
" <td>0.064741</td>\n",
" <td>0.022501</td>\n",
" <td>0.026530</td>\n",
" <td>0.018291</td>\n",
" <td>0.005453</td>\n",
" <td>0.008732</td>\n",
" <td>0.001900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td>0.007123</td>\n",
" <td>0.002123</td>\n",
" <td>0.003724</td>\n",
" <td>0.012489</td>\n",
" <td>0.048142</td>\n",
" <td>0.160540</td>\n",
" <td>0.182052</td>\n",
" <td>0.409602</td>\n",
" <td>0.092152</td>\n",
" <td>0.019968</td>\n",
" <td>0.024505</td>\n",
" <td>0.022260</td>\n",
" <td>0.005988</td>\n",
" <td>0.007256</td>\n",
" <td>0.002077</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118</th>\n",
" <td>0.007911</td>\n",
" <td>0.002188</td>\n",
" <td>0.003226</td>\n",
" <td>0.010458</td>\n",
" <td>0.077552</td>\n",
" <td>0.167267</td>\n",
" <td>0.272523</td>\n",
" <td>0.291606</td>\n",
" <td>0.088597</td>\n",
" <td>0.022089</td>\n",
" <td>0.019743</td>\n",
" <td>0.018421</td>\n",
" <td>0.010608</td>\n",
" <td>0.005471</td>\n",
" <td>0.002339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td>0.009024</td>\n",
" <td>0.002174</td>\n",
" <td>0.006711</td>\n",
" <td>0.029069</td>\n",
" <td>0.140957</td>\n",
" <td>0.193233</td>\n",
" <td>0.241961</td>\n",
" <td>0.236097</td>\n",
" <td>0.053844</td>\n",
" <td>0.017846</td>\n",
" <td>0.030766</td>\n",
" <td>0.018256</td>\n",
" <td>0.010356</td>\n",
" <td>0.007156</td>\n",
" <td>0.002548</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td>0.013152</td>\n",
" <td>0.003265</td>\n",
" <td>0.012240</td>\n",
" <td>0.036879</td>\n",
" <td>0.139909</td>\n",
" <td>0.188424</td>\n",
" <td>0.197292</td>\n",
" <td>0.219021</td>\n",
" <td>0.070328</td>\n",
" <td>0.029804</td>\n",
" <td>0.035486</td>\n",
" <td>0.026673</td>\n",
" <td>0.011274</td>\n",
" <td>0.009793</td>\n",
" <td>0.006460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td>0.012992</td>\n",
" <td>0.002633</td>\n",
" <td>0.014772</td>\n",
" <td>0.051202</td>\n",
" <td>0.130984</td>\n",
" <td>0.174466</td>\n",
" <td>0.199585</td>\n",
" <td>0.219951</td>\n",
" <td>0.066008</td>\n",
" <td>0.018410</td>\n",
" <td>0.051950</td>\n",
" <td>0.027580</td>\n",
" <td>0.010613</td>\n",
" <td>0.011004</td>\n",
" <td>0.007848</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122</th>\n",
" <td>0.006958</td>\n",
" <td>0.003734</td>\n",
" <td>0.014255</td>\n",
" <td>0.044129</td>\n",
" <td>0.212631</td>\n",
" <td>0.181365</td>\n",
" <td>0.228073</td>\n",
" <td>0.177632</td>\n",
" <td>0.028573</td>\n",
" <td>0.013940</td>\n",
" <td>0.052163</td>\n",
" <td>0.014521</td>\n",
" <td>0.012461</td>\n",
" <td>0.007210</td>\n",
" <td>0.002354</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123</th>\n",
" <td>0.005981</td>\n",
" <td>0.003979</td>\n",
" <td>0.012780</td>\n",
" <td>0.043980</td>\n",
" <td>0.196571</td>\n",
" <td>0.234476</td>\n",
" <td>0.233932</td>\n",
" <td>0.160091</td>\n",
" <td>0.024039</td>\n",
" <td>0.007841</td>\n",
" <td>0.040799</td>\n",
" <td>0.013534</td>\n",
" <td>0.011757</td>\n",
" <td>0.008575</td>\n",
" <td>0.001666</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td>0.007350</td>\n",
" <td>0.004150</td>\n",
" <td>0.020252</td>\n",
" <td>0.065181</td>\n",
" <td>0.148432</td>\n",
" <td>0.139802</td>\n",
" <td>0.153331</td>\n",
" <td>0.235632</td>\n",
" <td>0.072658</td>\n",
" <td>0.020765</td>\n",
" <td>0.059332</td>\n",
" <td>0.031483</td>\n",
" <td>0.020661</td>\n",
" <td>0.017117</td>\n",
" <td>0.003854</td>\n",
" </tr>\n",
" <tr>\n",
" <th>125</th>\n",
" <td>0.008479</td>\n",
" <td>0.003069</td>\n",
" <td>0.015253</td>\n",
" <td>0.037649</td>\n",
" <td>0.161600</td>\n",
" <td>0.138374</td>\n",
" <td>0.154515</td>\n",
" <td>0.273486</td>\n",
" <td>0.092871</td>\n",
" <td>0.022581</td>\n",
" <td>0.042561</td>\n",
" <td>0.020687</td>\n",
" <td>0.014274</td>\n",
" <td>0.010868</td>\n",
" <td>0.003733</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126</th>\n",
" <td>0.014171</td>\n",
" <td>0.002404</td>\n",
" <td>0.009877</td>\n",
" <td>0.018544</td>\n",
" <td>0.075304</td>\n",
" <td>0.124769</td>\n",
" <td>0.160166</td>\n",
" <td>0.318241</td>\n",
" <td>0.169709</td>\n",
" <td>0.024867</td>\n",
" <td>0.028046</td>\n",
" <td>0.025244</td>\n",
" <td>0.011261</td>\n",
" <td>0.012984</td>\n",
" <td>0.004414</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127</th>\n",
" <td>0.006749</td>\n",
" <td>0.001103</td>\n",
" <td>0.005632</td>\n",
" <td>0.007955</td>\n",
" <td>0.082876</td>\n",
" <td>0.200317</td>\n",
" <td>0.198183</td>\n",
" <td>0.337929</td>\n",
" <td>0.094330</td>\n",
" <td>0.014387</td>\n",
" <td>0.021977</td>\n",
" <td>0.013566</td>\n",
" <td>0.005540</td>\n",
" <td>0.007576</td>\n",
" <td>0.001878</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>128 rows × 15 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 \\\n",
"0 0.002889 0.000262 0.000113 0.000332 0.000874 0.013362 0.026816 \n",
"1 0.001209 0.000249 0.000178 0.000570 0.002753 0.035972 0.037394 \n",
"2 0.001463 0.000650 0.001440 0.002801 0.021948 0.214341 0.151857 \n",
"3 0.000915 0.000262 0.000964 0.002737 0.009546 0.124081 0.133722 \n",
"4 0.001300 0.000263 0.000845 0.002270 0.002880 0.043798 0.049535 \n",
"5 0.003215 0.001923 0.002388 0.003413 0.005225 0.066776 0.086268 \n",
"6 0.002994 0.000972 0.002336 0.003231 0.007752 0.049721 0.055319 \n",
"7 0.003869 0.001024 0.001581 0.003344 0.004220 0.018234 0.032417 \n",
"8 0.002422 0.002358 0.004797 0.003065 0.013478 0.049664 0.064920 \n",
"9 0.000937 0.001034 0.000557 0.000883 0.002400 0.013187 0.026888 \n",
"10 0.000696 0.001453 0.000735 0.000683 0.002806 0.013395 0.048595 \n",
"11 0.001585 0.001175 0.001208 0.001297 0.007143 0.070275 0.115415 \n",
"12 0.001582 0.000978 0.000812 0.001945 0.006477 0.056873 0.111269 \n",
"13 0.000581 0.000669 0.000410 0.000672 0.002426 0.013207 0.026962 \n",
"14 0.000584 0.001342 0.001889 0.001014 0.008103 0.016353 0.032403 \n",
"15 0.001876 0.000791 0.000661 0.000530 0.015188 0.031775 0.109170 \n",
"16 0.002000 0.000724 0.001064 0.001519 0.011146 0.011766 0.021432 \n",
"17 0.006862 0.001878 0.002407 0.002745 0.019984 0.051714 0.095929 \n",
"18 0.003598 0.001317 0.004068 0.004550 0.007880 0.021334 0.044755 \n",
"19 0.001463 0.001585 0.010295 0.007534 0.026094 0.029908 0.076007 \n",
"20 0.003687 0.002116 0.008042 0.009403 0.036685 0.054722 0.103267 \n",
"21 0.000684 0.000297 0.000536 0.001476 0.015776 0.042066 0.034512 \n",
"22 0.002152 0.000516 0.000668 0.001525 0.009948 0.029619 0.029172 \n",
"23 0.002113 0.000095 0.000114 0.000500 0.001584 0.016861 0.011649 \n",
"24 0.007268 0.000238 0.000130 0.000781 0.001389 0.016094 0.016393 \n",
"25 0.000597 0.000105 0.000093 0.000340 0.000879 0.011234 0.010297 \n",
"26 0.000772 0.000134 0.000586 0.001547 0.005980 0.027154 0.055915 \n",
"27 0.000468 0.000041 0.000333 0.001973 0.004937 0.048043 0.051649 \n",
"28 0.000305 0.000135 0.000257 0.000235 0.001835 0.005513 0.007199 \n",
"29 0.000302 0.000098 0.000135 0.000298 0.000787 0.004093 0.005622 \n",
".. ... ... ... ... ... ... ... \n",
"98 0.005951 0.005064 0.004709 0.012446 0.038810 0.292921 0.202148 \n",
"99 0.012083 0.009363 0.009550 0.032847 0.042767 0.176283 0.129478 \n",
"100 0.012490 0.008666 0.010667 0.025477 0.044867 0.111059 0.097862 \n",
"101 0.017078 0.005925 0.008030 0.020460 0.037724 0.120288 0.147378 \n",
"102 0.009755 0.003072 0.003409 0.013082 0.021136 0.080980 0.080735 \n",
"103 0.014257 0.004840 0.004938 0.014935 0.027716 0.133843 0.132579 \n",
"104 0.003738 0.001680 0.002349 0.010269 0.033786 0.215440 0.188105 \n",
"105 0.008697 0.002618 0.002177 0.008209 0.043952 0.232946 0.204515 \n",
"106 0.007266 0.002043 0.002538 0.012946 0.027433 0.182838 0.162091 \n",
"107 0.007306 0.002323 0.002576 0.007011 0.037841 0.193709 0.174264 \n",
"108 0.018556 0.004530 0.004751 0.017410 0.023363 0.132356 0.097402 \n",
"109 0.021885 0.005318 0.004059 0.014414 0.019601 0.085293 0.064110 \n",
"110 0.014114 0.012873 0.010526 0.017478 0.069933 0.212864 0.171863 \n",
"111 0.009336 0.007401 0.009502 0.018849 0.056976 0.183016 0.187676 \n",
"112 0.007150 0.004227 0.005983 0.015288 0.045940 0.296108 0.203347 \n",
"113 0.009885 0.002930 0.002925 0.012656 0.020277 0.163696 0.099853 \n",
"114 0.003168 0.002068 0.002920 0.007215 0.037575 0.264716 0.213504 \n",
"115 0.008044 0.004917 0.007634 0.015607 0.048700 0.172218 0.109372 \n",
"116 0.004359 0.002325 0.003371 0.008491 0.043149 0.213245 0.176399 \n",
"117 0.007123 0.002123 0.003724 0.012489 0.048142 0.160540 0.182052 \n",
"118 0.007911 0.002188 0.003226 0.010458 0.077552 0.167267 0.272523 \n",
"119 0.009024 0.002174 0.006711 0.029069 0.140957 0.193233 0.241961 \n",
"120 0.013152 0.003265 0.012240 0.036879 0.139909 0.188424 0.197292 \n",
"121 0.012992 0.002633 0.014772 0.051202 0.130984 0.174466 0.199585 \n",
"122 0.006958 0.003734 0.014255 0.044129 0.212631 0.181365 0.228073 \n",
"123 0.005981 0.003979 0.012780 0.043980 0.196571 0.234476 0.233932 \n",
"124 0.007350 0.004150 0.020252 0.065181 0.148432 0.139802 0.153331 \n",
"125 0.008479 0.003069 0.015253 0.037649 0.161600 0.138374 0.154515 \n",
"126 0.014171 0.002404 0.009877 0.018544 0.075304 0.124769 0.160166 \n",
"127 0.006749 0.001103 0.005632 0.007955 0.082876 0.200317 0.198183 \n",
"\n",
" 7 8 9 10 11 12 13 \\\n",
"0 0.464476 0.392945 0.072178 0.004416 0.015483 0.000817 0.003132 \n",
"1 0.764745 0.091403 0.034134 0.012069 0.016184 0.001231 0.001560 \n",
"2 0.482169 0.079550 0.009009 0.016031 0.010532 0.002127 0.005760 \n",
"3 0.593238 0.094084 0.010174 0.009126 0.014326 0.000958 0.005399 \n",
"4 0.629151 0.168599 0.043498 0.010348 0.040579 0.001812 0.004121 \n",
"5 0.545950 0.132445 0.068370 0.016058 0.051442 0.005756 0.007596 \n",
"6 0.592422 0.157993 0.051747 0.022557 0.043084 0.003378 0.004385 \n",
"7 0.581289 0.203282 0.073275 0.022607 0.045145 0.002617 0.004343 \n",
"8 0.568712 0.161477 0.042030 0.046157 0.021328 0.007322 0.008043 \n",
"9 0.710773 0.172089 0.027989 0.017082 0.017026 0.002443 0.004691 \n",
"10 0.729796 0.112151 0.041433 0.016698 0.018658 0.004329 0.006065 \n",
"11 0.524635 0.143762 0.055408 0.032890 0.030774 0.004361 0.007500 \n",
"12 0.523270 0.193383 0.028459 0.029239 0.034106 0.002812 0.005876 \n",
"13 0.505740 0.334069 0.050117 0.022814 0.036167 0.002289 0.002321 \n",
"14 0.596749 0.204039 0.079260 0.030771 0.021365 0.002941 0.002345 \n",
"15 0.534323 0.256911 0.025053 0.010340 0.007189 0.001494 0.003045 \n",
"16 0.450818 0.428953 0.043911 0.015116 0.007023 0.000915 0.001940 \n",
"17 0.358381 0.369931 0.036588 0.017278 0.019750 0.006067 0.003543 \n",
"18 0.396489 0.402196 0.059507 0.018962 0.025530 0.002319 0.003132 \n",
"19 0.471224 0.221922 0.059176 0.052063 0.031353 0.006133 0.003190 \n",
"20 0.461837 0.191535 0.042185 0.054491 0.020491 0.006196 0.003343 \n",
"21 0.720035 0.140629 0.008838 0.028773 0.003289 0.001162 0.001582 \n",
"22 0.604726 0.265445 0.016037 0.028722 0.005465 0.001608 0.003275 \n",
"23 0.623052 0.303452 0.025280 0.006112 0.007360 0.000370 0.001037 \n",
"24 0.478390 0.411849 0.043488 0.008673 0.012021 0.000471 0.001505 \n",
"25 0.647319 0.267520 0.040340 0.009551 0.009212 0.000427 0.001581 \n",
"26 0.701369 0.159252 0.019929 0.009854 0.014688 0.001070 0.001044 \n",
"27 0.789747 0.079604 0.007369 0.005465 0.009175 0.000372 0.000663 \n",
"28 0.571161 0.365943 0.030701 0.008764 0.006503 0.000545 0.000572 \n",
"29 0.583510 0.347018 0.037687 0.010870 0.008513 0.000368 0.000490 \n",
".. ... ... ... ... ... ... ... \n",
"98 0.306325 0.027954 0.020051 0.046290 0.020704 0.005912 0.008118 \n",
"99 0.356869 0.048585 0.042097 0.076583 0.034356 0.009283 0.013826 \n",
"100 0.425701 0.058746 0.068536 0.070689 0.034908 0.012580 0.009955 \n",
"101 0.341156 0.096774 0.056462 0.065353 0.047448 0.014276 0.010095 \n",
"102 0.461166 0.150579 0.053019 0.060831 0.037480 0.005660 0.008724 \n",
"103 0.364001 0.118931 0.049530 0.054856 0.044964 0.009591 0.013625 \n",
"104 0.401550 0.054096 0.021966 0.031242 0.021022 0.007193 0.005934 \n",
"105 0.307876 0.106385 0.020108 0.029689 0.014908 0.005784 0.009434 \n",
"106 0.369198 0.144422 0.021074 0.026757 0.022389 0.004991 0.010541 \n",
"107 0.368229 0.129330 0.025554 0.021307 0.017691 0.005673 0.005215 \n",
"108 0.374592 0.149907 0.058947 0.040111 0.049470 0.009569 0.011719 \n",
"109 0.372145 0.178855 0.102141 0.053135 0.046302 0.007855 0.013709 \n",
"110 0.235218 0.079648 0.051693 0.049476 0.027906 0.016730 0.021295 \n",
"111 0.274916 0.110657 0.037982 0.040759 0.029551 0.011673 0.016540 \n",
"112 0.252054 0.070659 0.022142 0.023900 0.024324 0.010055 0.015052 \n",
"113 0.397851 0.118069 0.061566 0.041452 0.046942 0.005816 0.010985 \n",
"114 0.319680 0.067740 0.017075 0.027577 0.019909 0.005508 0.009567 \n",
"115 0.392536 0.096442 0.039451 0.042452 0.030450 0.013520 0.014595 \n",
"116 0.400512 0.064741 0.022501 0.026530 0.018291 0.005453 0.008732 \n",
"117 0.409602 0.092152 0.019968 0.024505 0.022260 0.005988 0.007256 \n",
"118 0.291606 0.088597 0.022089 0.019743 0.018421 0.010608 0.005471 \n",
"119 0.236097 0.053844 0.017846 0.030766 0.018256 0.010356 0.007156 \n",
"120 0.219021 0.070328 0.029804 0.035486 0.026673 0.011274 0.009793 \n",
"121 0.219951 0.066008 0.018410 0.051950 0.027580 0.010613 0.011004 \n",
"122 0.177632 0.028573 0.013940 0.052163 0.014521 0.012461 0.007210 \n",
"123 0.160091 0.024039 0.007841 0.040799 0.013534 0.011757 0.008575 \n",
"124 0.235632 0.072658 0.020765 0.059332 0.031483 0.020661 0.017117 \n",
"125 0.273486 0.092871 0.022581 0.042561 0.020687 0.014274 0.010868 \n",
"126 0.318241 0.169709 0.024867 0.028046 0.025244 0.011261 0.012984 \n",
"127 0.337929 0.094330 0.014387 0.021977 0.013566 0.005540 0.007576 \n",
"\n",
" 14 \n",
"0 0.001904 \n",
"1 0.000347 \n",
"2 0.000323 \n",
"3 0.000468 \n",
"4 0.001000 \n",
"5 0.003174 \n",
"6 0.002109 \n",
"7 0.002753 \n",
"8 0.004228 \n",
"9 0.002018 \n",
"10 0.002508 \n",
"11 0.002571 \n",
"12 0.002918 \n",
"13 0.001555 \n",
"14 0.000843 \n",
"15 0.001653 \n",
"16 0.001673 \n",
"17 0.006943 \n",
"18 0.004364 \n",
"19 0.002053 \n",
"20 0.001999 \n",
"21 0.000345 \n",
"22 0.001120 \n",
"23 0.000420 \n",
"24 0.001311 \n",
"25 0.000504 \n",
"26 0.000707 \n",
"27 0.000159 \n",
"28 0.000331 \n",
"29 0.000210 \n",
".. ... \n",
"98 0.002597 \n",
"99 0.006030 \n",
"100 0.007796 \n",
"101 0.011552 \n",
"102 0.010373 \n",
"103 0.011395 \n",
"104 0.001631 \n",
"105 0.002703 \n",
"106 0.003473 \n",
"107 0.001972 \n",
"108 0.007316 \n",
"109 0.011179 \n",
"110 0.008384 \n",
"111 0.005166 \n",
"112 0.003770 \n",
"113 0.005099 \n",
"114 0.001779 \n",
"115 0.004061 \n",
"116 0.001900 \n",
"117 0.002077 \n",
"118 0.002339 \n",
"119 0.002548 \n",
"120 0.006460 \n",
"121 0.007848 \n",
"122 0.002354 \n",
"123 0.001666 \n",
"124 0.003854 \n",
"125 0.003733 \n",
"126 0.004414 \n",
"127 0.001878 \n",
"\n",
"[128 rows x 15 columns]"
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfpd.DataFrame(result[0])"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([-0.53430105, -0.59176575, -0.76506821, -0.76506821, -0.76506821,\n",
" -0.82727325, -0.76427716, -0.76427716, -0.64376723, -0.5229167 ,\n",
" -0.5229167 , -0.5229167 , -0.43492736, -0.40313842, -0.40809833,\n",
" -0.40809833, -0.28951139, -0.22236469, -0.22236469, -0.24343892,\n",
" -0.24343892, -0.2272934 , -0.2272934 , -0.16265476, -0.2345496 ,\n",
" -0.2345496 , -0.2345496 , -0.37286782, -0.34749746, -0.34749746,\n",
" -0.29982846, -0.29982846, -0.2379019 , -0.2379019 , -0.2287211 ,\n",
" -0.22863048, -0.22863048, -0.22863048, -0.2451124 , -0.20696927,\n",
" -0.14535472, -0.14535472, -0.14535472, -0.0536792 , -0.00945108,\n",
" -0.00945108, 0. , 0. , 0. , 0. ,\n",
" 0. , 0. , 0. , 0. , 0. ,\n",
" 0. , 0. , 0. , 0.0017335 , 0.0017335 ,\n",
" 0.1791818 , 0.33618481, 0.33618481, 0.49266367, 0.49266367,\n",
" 0.62431059, 0.62431059, 0.66889275, 0.61948459, 0.61948459,\n",
" 0.54208761, 0.54208761, 0.47882968, 0.47882968, 0.401685 ,\n",
" 0.31291418, 0.31291418, 0.17782517, 0.17782517, 0.13906586,\n",
" 0.12261757, 0.12261757, 0.12368338, 0.12368338, 0.12569279,\n",
" 0.15573568, 0.15573568, 0.15573568, 0.20957223, 0.29901293,\n",
" 0.29901293, 0.35082468, 0.35082468, 0.40332928, 0.49323276,\n",
" 0.49323276, 0.49756169, 0.49756169, 0.46929123, 0.37497287,\n",
" 0.37497287, 0.27069669, 0.27069669, 0.16277528, 0.16277528,\n",
" 0.09740735, 0.00905935, 0.00905935, 0. , 0. ,\n",
" 0. , 0. , -0.02044588, -0.02044588, -0.06786578,\n",
" -0.0701909 , -0.0701909 , -0.05411462, -0.05411462, -0.15435511,\n",
" -0.19592217, -0.19592217, -0.19592217, -0.25167117, -0.36782348,\n",
" -0.49633263, -0.49633263, -0.49633263])]"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r = next(t)\n",
"r[1]"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'cam/image_array': array([[[192, 156, 106],\n",
" [196, 160, 108],\n",
" [200, 167, 114],\n",
" ..., \n",
" [199, 148, 91],\n",
" [195, 144, 87],\n",
" [187, 136, 79]],\n",
" \n",
" [[188, 147, 101],\n",
" [205, 164, 118],\n",
" [195, 157, 108],\n",
" ..., \n",
" [199, 148, 91],\n",
" [195, 144, 87],\n",
" [187, 136, 79]],\n",
" \n",
" [[187, 139, 101],\n",
" [202, 154, 114],\n",
" [197, 150, 108],\n",
" ..., \n",
" [199, 148, 91],\n",
" [194, 143, 86],\n",
" [187, 136, 79]],\n",
" \n",
" ..., \n",
" [[ 71, 26, 7],\n",
" [ 83, 34, 17],\n",
" [ 97, 44, 28],\n",
" ..., \n",
" [ 46, 18, 4],\n",
" [ 42, 14, 2],\n",
" [ 35, 7, 0]],\n",
" \n",
" [[ 65, 22, 3],\n",
" [ 83, 38, 19],\n",
" [ 94, 45, 28],\n",
" ..., \n",
" [ 46, 18, 4],\n",
" [ 41, 13, 1],\n",
" [ 34, 6, 0]],\n",
" \n",
" [[ 62, 21, 1],\n",
" [ 82, 39, 20],\n",
" [ 92, 45, 27],\n",
" ..., \n",
" [ 46, 18, 4],\n",
" [ 41, 13, 1],\n",
" [ 34, 6, 0]]], dtype=uint8),\n",
" 'user/angle': -0.30163385810095467,\n",
" 'user/mode': 'user',\n",
" 'user/throttle': 0.42379201929669896}"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r get_record_from_df(3, df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'/home/wroscoe/d2/data/tub_55_17-09-16/'"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def get_record(ix, T):\n",
" pass\n",
" \n",
"T.path"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe414bfca90>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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S6j5QmGeJrTGA8huc9zCc/Gu1mrUXKq0ozQXCDhp6qjHsXaEqA5z5R5XzIwT8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\nFApFC/4/oCWhImSE3ssAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe414c19208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df[['user/angle', 'user/throttle']][0:500].plot()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df['pilot/throttle'] = (1-abs(df['user/angle']))/4 + .38"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe414886f98>"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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sfBV+fxWiOpFwufyy7mrZEmXtW7D9c+9P7qXG4AyJ/XDUh5V/oMMFhzlk0Mkc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\nY1UAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe41480d2e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df[['user/angle', 'user/throttle', 'pilot/throttle']][0:500].plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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