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model_analysis/model_car_test_cases.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model Car Test Cases "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from scipy.stats import stats\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load the Data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>chatter__data</th>\n",
" <th>diagnostics__header_frame_id</th>\n",
" <th>diagnostics__header_seq</th>\n",
" <th>diagnostics__header_stamp_nsecs</th>\n",
" <th>diagnostics__header_stamp_secs</th>\n",
" <th>drive_parameters__angle</th>\n",
" <th>drive_parameters__velocity</th>\n",
" <th>drive_pwm__pwm_angle</th>\n",
" <th>drive_pwm__pwm_drive</th>\n",
" <th>...</th>\n",
" <th>rosout_agg__file</th>\n",
" <th>rosout_agg__function</th>\n",
" <th>rosout_agg__header_frame_id</th>\n",
" <th>rosout_agg__header_seq</th>\n",
" <th>rosout_agg__header_stamp_nsecs</th>\n",
" <th>rosout_agg__header_stamp_secs</th>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2018-01-28 20:06:12.433865216</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2018-01-28 20:06:12.544487680</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" </tr>\n",
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" <th>3</th>\n",
" <td>2018-01-28 20:06:12.602825728</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
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" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1970-01-01 00:10:12.591298000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 71 columns</p>\n",
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"text/plain": [
" Unnamed: 0 chatter__data diagnostics__header_frame_id \\\n",
"0 2018-01-28 20:06:12.336234496 NaN NaN \n",
"1 2018-01-28 20:06:12.433865216 NaN NaN \n",
"2 2018-01-28 20:06:12.544487680 NaN NaN \n",
"3 2018-01-28 20:06:12.602825728 NaN NaN \n",
"4 1970-01-01 00:10:12.591298000 NaN NaN \n",
"\n",
" diagnostics__header_seq diagnostics__header_stamp_nsecs \\\n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"\n",
" diagnostics__header_stamp_secs drive_parameters__angle \\\n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"\n",
" drive_parameters__velocity drive_pwm__pwm_angle drive_pwm__pwm_drive \\\n",
"0 NaN NaN NaN \n",
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"3 NaN NaN NaN \n",
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" ... rosout_agg__file rosout_agg__function \\\n",
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"1 ... NaN NaN \n",
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"\n",
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"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"\n",
" rosout_agg__header_stamp_nsecs rosout_agg__header_stamp_secs \\\n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"\n",
" rosout_agg__level rosout_agg__line rosout_agg__msg rosout_agg__name \n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
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"3 NaN NaN NaN NaN \n",
"4 NaN NaN NaN NaN \n",
"\n",
"[5 rows x 71 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Load the Data\n",
"base_path = './data/'\n",
"sensor_data = pd.read_csv(base_path + 'On500MSRev100MS.csv')\n",
"\n",
"# Convert time column into datetime data types\n",
"sensor_data.iloc[:, 0] = pd.to_datetime(sensor_data.iloc[:, 0])\n",
"\n",
"# Data Preview\n",
"sensor_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Unnamed: 0', 'chatter__data', 'diagnostics__header_frame_id',\n",
" 'diagnostics__header_seq', 'diagnostics__header_stamp_nsecs',\n",
" 'diagnostics__header_stamp_secs', 'drive_parameters__angle',\n",
" 'drive_parameters__velocity', 'drive_pwm__pwm_angle',\n",
" 'drive_pwm__pwm_drive', 'imu__angular_velocity_covariance0',\n",
" 'imu__angular_velocity_covariance1',\n",
" 'imu__angular_velocity_covariance2',\n",
" 'imu__angular_velocity_covariance3',\n",
" 'imu__angular_velocity_covariance4',\n",
" 'imu__angular_velocity_covariance5',\n",
" 'imu__angular_velocity_covariance6',\n",
" 'imu__angular_velocity_covariance7',\n",
" 'imu__angular_velocity_covariance8', 'imu__angular_velocity_x',\n",
" 'imu__angular_velocity_y', 'imu__angular_velocity_z',\n",
" 'imu__header_frame_id', 'imu__header_seq',\n",
" 'imu__header_stamp_nsecs', 'imu__header_stamp_secs',\n",
" 'imu__linear_acceleration_covariance0',\n",
" 'imu__linear_acceleration_covariance1',\n",
" 'imu__linear_acceleration_covariance2',\n",
" 'imu__linear_acceleration_covariance3',\n",
" 'imu__linear_acceleration_covariance4',\n",
" 'imu__linear_acceleration_covariance5',\n",
" 'imu__linear_acceleration_covariance6',\n",
" 'imu__linear_acceleration_covariance7',\n",
" 'imu__linear_acceleration_covariance8',\n",
" 'imu__linear_acceleration_x', 'imu__linear_acceleration_y',\n",
" 'imu__linear_acceleration_z', 'imu__orientation_covariance0',\n",
" 'imu__orientation_covariance1', 'imu__orientation_covariance2',\n",
" 'imu__orientation_covariance3', 'imu__orientation_covariance4',\n",
" 'imu__orientation_covariance5', 'imu__orientation_covariance6',\n",
" 'imu__orientation_covariance7', 'imu__orientation_covariance8',\n",
" 'imu__orientation_w', 'imu__orientation_x', 'imu__orientation_y',\n",
" 'imu__orientation_z', 'rosout__file', 'rosout__function',\n",
" 'rosout__header_frame_id', 'rosout__header_seq',\n",
" 'rosout__header_stamp_nsecs', 'rosout__header_stamp_secs',\n",
" 'rosout__level', 'rosout__line', 'rosout__msg', 'rosout__name',\n",
" 'rosout_agg__file', 'rosout_agg__function',\n",
" 'rosout_agg__header_frame_id', 'rosout_agg__header_seq',\n",
" 'rosout_agg__header_stamp_nsecs', 'rosout_agg__header_stamp_secs',\n",
" 'rosout_agg__level', 'rosout_agg__line', 'rosout_agg__msg',\n",
" 'rosout_agg__name'], dtype=object)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sensor_data.columns.values"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
" Unnamed: 0 drive_pwm__pwm_angle drive_pwm__pwm_drive\n",
"0 2018-01-28 20:06:12.336234496 NaN NaN\n",
"1 2018-01-28 20:06:12.433865216 NaN NaN\n",
"2 2018-01-28 20:06:12.544487680 NaN NaN\n",
"3 2018-01-28 20:06:12.602825728 NaN NaN\n",
"4 1970-01-01 00:10:12.591298000 NaN NaN"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# extract the particular data of interest\n",
"pwm_data = sensor_data.loc[:, ['Unnamed: 0', 'drive_pwm__pwm_angle', 'drive_pwm__pwm_drive']]\n",
"imu_linear_accel = sensor_data.loc[:, ['imu__linear_acceleration_x', 'imu__linear_acceleration_y', 'imu__linear_acceleration_z']]\n",
"\n",
"# Forward Fill PWM values\n",
"pwm_data = pwm_data.fillna(method='ffill')\n",
"pwm_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
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"text/plain": [
" Unnamed: 0 drive_pwm__pwm_angle drive_pwm__pwm_drive \\\n",
"7 1970-01-01 00:42:00.864973000 NaN NaN \n",
"8 1970-01-01 00:42:00.885788999 NaN NaN \n",
"10 1970-01-01 00:42:00.907567000 NaN NaN \n",
"11 1970-01-01 00:42:00.926911999 NaN NaN \n",
"12 1970-01-01 00:42:00.947696000 NaN NaN \n",
"\n",
" imu__linear_acceleration_x imu__linear_acceleration_y \\\n",
"7 0.235191 0.235191 \n",
"8 0.078525 0.117595 \n",
"10 0.039071 0.196120 \n",
"11 0.078525 0.235191 \n",
"12 0.117595 0.157049 \n",
"\n",
" imu__linear_acceleration_z \n",
"7 8.472231 \n",
"8 8.472231 \n",
"10 8.197969 \n",
"11 8.315565 \n",
"12 8.511685 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Concat pwm with accel data\n",
"pwm_accel_data = pd.concat([pwm_data, imu_linear_accel], axis=1)\n",
"pwm_accel_data = pwm_accel_data.dropna(how='all', subset=['drive_pwm__pwm_angle', 'drive_pwm__pwm_drive', 'imu__linear_acceleration_x', 'imu__linear_acceleration_y', 'imu__linear_acceleration_z'])\n",
"pwm_accel_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"imu__linear_acceleration_x 0.125486\n",
"imu__linear_acceleration_y 0.198093\n",
"imu__linear_acceleration_z 8.401788\n",
"dtype: float64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Calculate gravity for each axis\n",
"first_couple_accel = pwm_accel_data.iloc[0:20, 3:]\n",
"means = first_couple_accel.apply(np.mean, axis=0)\n",
"means"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8.405060192059393"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# verify this is indeed gravity\n",
"np.sqrt(means[0]**2 + means[1]**2 + means[2]**2)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.086415</td>\n",
" <td>-0.001973</td>\n",
" <td>-0.203819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1970-01-01 00:42:00.926911999</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.046962</td>\n",
" <td>0.037098</td>\n",
" <td>-0.086224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>1970-01-01 00:42:00.947696000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.007891</td>\n",
" <td>-0.041043</td>\n",
" <td>0.109896</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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],
"text/plain": [
" Unnamed: 0 drive_pwm__pwm_angle drive_pwm__pwm_drive \\\n",
"7 1970-01-01 00:42:00.864973000 NaN NaN \n",
"8 1970-01-01 00:42:00.885788999 NaN NaN \n",
"10 1970-01-01 00:42:00.907567000 NaN NaN \n",
"11 1970-01-01 00:42:00.926911999 NaN NaN \n",
"12 1970-01-01 00:42:00.947696000 NaN NaN \n",
"\n",
" imu__linear_acceleration_x imu__linear_acceleration_y \\\n",
"7 0.109705 0.037098 \n",
"8 -0.046962 -0.080497 \n",
"10 -0.086415 -0.001973 \n",
"11 -0.046962 0.037098 \n",
"12 -0.007891 -0.041043 \n",
"\n",
" imu__linear_acceleration_z \n",
"7 0.070442 \n",
"8 0.070442 \n",
"10 -0.203819 \n",
"11 -0.086224 \n",
"12 0.109896 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Subract gravity from imu values\n",
"pwm_accel_data.loc[:, 'imu__linear_acceleration_x'] -= means[0]\n",
"pwm_accel_data.loc[:, 'imu__linear_acceleration_y'] -= means[1]\n",
"pwm_accel_data.loc[:, 'imu__linear_acceleration_z'] -= means[2]\n",
"pwm_accel_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To calculate velocity from acceleration, we must integrate acceleration over time. Given we have discrete data points, we must take a discrete approach. We will use the [Riemann Sum](https://en.wikipedia.org/wiki/Riemann_sum) method. Luckily, the sample rate for acceleration is very high, so we will have fairly accurate measures of instaneious velocit\n",
"\n",
"#### So how exactly is it going to be done?\n",
"\n",
"Since each row has a timestamp, we can take consecutive rows as a rectangle, where the base of the rectange can be the time difference between consecutive rows, and the height of the rectangle to be the acceleartion at that time. In this case we'll use right-hand riemann sums. For example:\n",
"\n",
"```python\n",
"t1, a1 = pwm_accel_data.iloc[0, [0, 3]]\n",
"t2, a2 = pwm_accel_data.iloc[1, [0, 3]]\n",
"rectange_base = t2.timestamp()-t1.timestamp() # timestamp converts to milliseconds\n",
"approx_instantaneous_velocity = rectange_base * a2 # a2 because right-hand riemann sums\n",
"```\n",
"\n",
"##### **Note**: I don't know what's the units of the accelerometer readings."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>drive_pwm__pwm_angle</th>\n",
" <th>drive_pwm__pwm_drive</th>\n",
" <th>imu__linear_acceleration_x</th>\n",
" <th>imu__linear_acceleration_y</th>\n",
" <th>imu__linear_acceleration_z</th>\n",
" <th>instant_velocity_x</th>\n",
" <th>instant_velocity_y</th>\n",
" <th>instant_velocity_z</th>\n",
" <th>speed</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1970-01-01 00:42:00.864973000</td>\n",
" <td>NaN</td>\n",
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" <td>0.109705</td>\n",
" <td>0.037098</td>\n",
" <td>0.070442</td>\n",
" <td>-0.000978</td>\n",
" <td>-0.001676</td>\n",
" <td>0.001466</td>\n",
" <td>0.002432</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1970-01-01 00:42:00.885788999</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.046962</td>\n",
" <td>-0.080497</td>\n",
" <td>0.070442</td>\n",
" <td>-0.001882</td>\n",
" <td>-0.000043</td>\n",
" <td>-0.004439</td>\n",
" <td>0.004821</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1970-01-01 00:42:00.907567000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.086415</td>\n",
" <td>-0.001973</td>\n",
" <td>-0.203819</td>\n",
" <td>-0.000908</td>\n",
" <td>0.000718</td>\n",
" <td>-0.001668</td>\n",
" <td>0.002030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1970-01-01 00:42:00.926911999</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.046962</td>\n",
" <td>0.037098</td>\n",
" <td>-0.086224</td>\n",
" <td>-0.000164</td>\n",
" <td>-0.000853</td>\n",
" <td>0.002284</td>\n",
" <td>0.002444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>1970-01-01 00:42:00.947696000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.007891</td>\n",
" <td>-0.041043</td>\n",
" <td>0.109896</td>\n",
" <td>0.001405</td>\n",
" <td>-0.000817</td>\n",
" <td>0.003749</td>\n",
" <td>0.004086</td>\n",
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"text/plain": [
" Unnamed: 0 drive_pwm__pwm_angle drive_pwm__pwm_drive \\\n",
"7 1970-01-01 00:42:00.864973000 NaN NaN \n",
"8 1970-01-01 00:42:00.885788999 NaN NaN \n",
"10 1970-01-01 00:42:00.907567000 NaN NaN \n",
"11 1970-01-01 00:42:00.926911999 NaN NaN \n",
"12 1970-01-01 00:42:00.947696000 NaN NaN \n",
"\n",
" imu__linear_acceleration_x imu__linear_acceleration_y \\\n",
"7 0.109705 0.037098 \n",
"8 -0.046962 -0.080497 \n",
"10 -0.086415 -0.001973 \n",
"11 -0.046962 0.037098 \n",
"12 -0.007891 -0.041043 \n",
"\n",
" imu__linear_acceleration_z instant_velocity_x instant_velocity_y \\\n",
"7 0.070442 -0.000978 -0.001676 \n",
"8 0.070442 -0.001882 -0.000043 \n",
"10 -0.203819 -0.000908 0.000718 \n",
"11 -0.086224 -0.000164 -0.000853 \n",
"12 0.109896 0.001405 -0.000817 \n",
"\n",
" instant_velocity_z speed \n",
"7 0.001466 0.002432 \n",
"8 -0.004439 0.004821 \n",
"10 -0.001668 0.002030 \n",
"11 0.002284 0.002444 \n",
"12 0.003749 0.004086 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#---------------------------------------\n",
"# Calculate time difference\n",
"#---------------------------------------\n",
"# Extract left hand side of rectangle\n",
"t1_series = pwm_accel_data.iloc[:, 0]\n",
"accel_x1 = pwm_accel_data.iloc[:, 3]\n",
"accel_y1 = pwm_accel_data.iloc[:, 4]\n",
"accel_z1 = pwm_accel_data.iloc[:, 5]\n",
"\n",
"# Shift the series to get left hand side of rectangle (pad left with zero)\n",
"# For example:\n",
"# Time\n",
"# t1 = [0, 2, 3, 4, 5]\n",
"# t2 = [2, 3, 4, 5, 0] # right side done next\n",
"# Acceleration\n",
"# x1 = [0, 9, 8, 7, 6]\n",
"# x2 = [9, 8, 7, 6, 0] # right side done next\n",
"t1_series_padded = np.pad(t1_series, (1,0), 'constant')\n",
"accel_x1_padded = np.pad(accel_x1, (1,0), 'constant')\n",
"accel_y1_padded = np.pad(accel_y1, (1,0), 'constant')\n",
"accel_z1_padded = np.pad(accel_z1, (1,0), 'constant')\n",
"\n",
"# Now the right hand side of rectangle\n",
"t2_series = np.pad(t1_series, (0,1), 'constant')\n",
"accel_x2 = np.pad(accel_x1, (0,1), 'constant')\n",
"accel_y2 = np.pad(accel_y1, (0,1), 'constant')\n",
"accel_z2 = np.pad(accel_z1, (0,1), 'constant')\n",
"\n",
"# Convert datetimes to timestamps (unix which is in seconds) (example in next cell)\n",
"tstamp2_series = (t2_series - np.datetime64('1970-01-01T00:00:00')) / np.timedelta64(1, 's')\n",
"tstamp1_series_padded = (t1_series_padded - np.datetime64('1970-01-01T00:00:00')) / np.timedelta64(1, 's')\n",
"\n",
"# Now substract them to get length of base\n",
"width_series = tstamp2_series - tstamp1_series_padded\n",
"\n",
"# Calculate the approximate velocity\n",
"approx_instant_velocity_x = width_series * accel_x2\n",
"approx_instant_velocity_y = width_series * accel_y2\n",
"approx_instant_velocity_z = width_series * accel_z2\n",
"approx_speed = np.sqrt(approx_instant_velocity_x**2 + \\\n",
" approx_instant_velocity_y**2 + \\\n",
" approx_instant_velocity_z**2)\n",
"\n",
"# Add the velocity columns to pmw dataframe\n",
"pwm_accel_data['instant_velocity_x'] = approx_instant_velocity_x[1:] # exclude the first one\n",
"pwm_accel_data['instant_velocity_y'] = approx_instant_velocity_y[1:] # exclude the first one\n",
"pwm_accel_data['instant_velocity_z'] = approx_instant_velocity_z[1:] # exclude the first one\n",
"pwm_accel_data['speed'] = approx_speed[1:]\n",
"\n",
"pwm_accel_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1970-01-01T00:42:00.864973000\n",
"2520864.973\n"
]
}
],
"source": [
"# Example of timestamp conversion\n",
"print(t2_series[0])\n",
"print((t2_series[0] - np.datetime64('1970-01-01T00:00:00')) / np.timedelta64(1, 'ms'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculate Correlation\n",
"\n",
"Let's get the correlation between **pwm** and **speed**.\n",
"\n",
"First, lets take a look at the scatter plot get an inutive sense of the data."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fa22b025a20>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa23388aa58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(pwm_accel_data.drive_pwm__pwm_drive, pwm_accel_data.speed)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ nan, 10486., 9503., 9831.])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sensor_data.drive_pwm__pwm_drive.unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It looks like we only have a few different PWM values (even before preprocessing). I don't think we can make an accurate estimate with these values."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>drive_pwm__pwm_angle</th>\n",
" <th>drive_pwm__pwm_drive</th>\n",
" <th>imu__linear_acceleration_x</th>\n",
" <th>imu__linear_acceleration_y</th>\n",
" <th>imu__linear_acceleration_z</th>\n",
" <th>instant_velocity_x</th>\n",
" <th>instant_velocity_y</th>\n",
" <th>instant_velocity_z</th>\n",
" <th>speed</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>352</th>\n",
" <td>1970-01-01 00:42:07.575413999</td>\n",
" <td>9831.0</td>\n",
" <td>10486.0</td>\n",
" <td>0.070634</td>\n",
" <td>0.037098</td>\n",
" <td>0.227492</td>\n",
" <td>0.000632</td>\n",
" <td>-0.000040</td>\n",
" <td>0.003775</td>\n",
" <td>0.003827</td>\n",
" </tr>\n",
" <tr>\n",
" <th>353</th>\n",
" <td>1970-01-01 00:42:07.595447000</td>\n",
" <td>9831.0</td>\n",
" <td>10486.0</td>\n",
" <td>0.031563</td>\n",
" <td>-0.001973</td>\n",
" <td>0.188421</td>\n",
" <td>-0.000165</td>\n",
" <td>-0.000860</td>\n",
" <td>-0.000988</td>\n",
" <td>0.001321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>354</th>\n",
" <td>1970-01-01 00:42:07.616406999</td>\n",
" <td>9831.0</td>\n",
" <td>10486.0</td>\n",
" <td>-0.007891</td>\n",
" <td>-0.041043</td>\n",
" <td>-0.047153</td>\n",
" <td>-0.001733</td>\n",
" <td>0.001535</td>\n",
" <td>-0.002520</td>\n",
" <td>0.003422</td>\n",
" </tr>\n",
" <tr>\n",
" <th>355</th>\n",
" <td>1970-01-01 00:42:07.636457999</td>\n",
" <td>9831.0</td>\n",
" <td>10486.0</td>\n",
" <td>-0.086415</td>\n",
" <td>0.076552</td>\n",
" <td>-0.125678</td>\n",
" <td>-0.000165</td>\n",
" <td>0.000778</td>\n",
" <td>-0.000161</td>\n",
" <td>0.000811</td>\n",
" </tr>\n",
" <tr>\n",
" <th>356</th>\n",
" <td>1970-01-01 00:42:07.657416999</td>\n",
" <td>9831.0</td>\n",
" <td>10486.0</td>\n",
" <td>-0.007891</td>\n",
" <td>0.037098</td>\n",
" <td>-0.007699</td>\n",
" <td>0.010535</td>\n",
" <td>-0.032151</td>\n",
" <td>0.029472</td>\n",
" <td>0.044869</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 drive_pwm__pwm_angle drive_pwm__pwm_drive \\\n",
"352 1970-01-01 00:42:07.575413999 9831.0 10486.0 \n",
"353 1970-01-01 00:42:07.595447000 9831.0 10486.0 \n",
"354 1970-01-01 00:42:07.616406999 9831.0 10486.0 \n",
"355 1970-01-01 00:42:07.636457999 9831.0 10486.0 \n",
"356 1970-01-01 00:42:07.657416999 9831.0 10486.0 \n",
"\n",
" imu__linear_acceleration_x imu__linear_acceleration_y \\\n",
"352 0.070634 0.037098 \n",
"353 0.031563 -0.001973 \n",
"354 -0.007891 -0.041043 \n",
"355 -0.086415 0.076552 \n",
"356 -0.007891 0.037098 \n",
"\n",
" imu__linear_acceleration_z instant_velocity_x instant_velocity_y \\\n",
"352 0.227492 0.000632 -0.000040 \n",
"353 0.188421 -0.000165 -0.000860 \n",
"354 -0.047153 -0.001733 0.001535 \n",
"355 -0.125678 -0.000165 0.000778 \n",
"356 -0.007699 0.010535 -0.032151 \n",
"\n",
" instant_velocity_z speed \n",
"352 0.003775 0.003827 \n",
"353 -0.000988 0.001321 \n",
"354 -0.002520 0.003422 \n",
"355 -0.000161 0.000811 \n",
"356 0.029472 0.044869 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pwm_for_correlation = pwm_accel_data.dropna()\n",
"pwm_for_correlation.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets show the correlation for **imu__linear_acceleration_x**"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"correlation (0.5832753987256681, 1.287182740143699e-18)\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fa22afbc780>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa23388ad30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print('correlation',\n",
" stats.pearsonr(pwm_for_correlation.drive_pwm__pwm_drive,\n",
" pwm_for_correlation.imu__linear_acceleration_x))\n",
"\n",
"plt.scatter(pwm_accel_data.drive_pwm__pwm_drive, pwm_accel_data.imu__linear_acceleration_x.abs())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's check to same for the corresponding velocity"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fa22ae8fcc0>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fa22af169b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pwm_accel_data.imu__linear_acceleration_x = pwm_accel_data.imu__linear_acceleration_x.abs()\n",
"grouped_stuff = pwm_accel_data.groupby('drive_pwm__pwm_drive').apply(np.mean)\n",
"grouped_stuff.head()\n",
"plt.scatter(grouped_stuff.drive_pwm__pwm_drive, grouped_stuff.imu__linear_acceleration_x.abs())"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/reynaldo/miniconda3/envs/autocar/lib/python3.6/site-packages/pandas/core/frame.py:3027: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" return super(DataFrame, self).rename(**kwargs)\n"
]
},
{
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" <td>-0.001973</td>\n",
" <td>-0.047153</td>\n",
" <td>0.049994</td>\n",
" <td>-0.000860</td>\n",
" <td>0.011345</td>\n",
" <td>0.051273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>369</th>\n",
" <td>1970-01-01 00:42:07.924277999</td>\n",
" <td>9831.0</td>\n",
" <td>10486.0</td>\n",
" <td>2.385003</td>\n",
" <td>-0.041043</td>\n",
" <td>0.541207</td>\n",
" <td>0.055805</td>\n",
" <td>0.004074</td>\n",
" <td>0.001478</td>\n",
" <td>0.055973</td>\n",
" </tr>\n",
" <tr>\n",
" <th>370</th>\n",
" <td>1970-01-01 00:42:07.945263000</td>\n",
" <td>9831.0</td>\n",
" <td>10486.0</td>\n",
" <td>2.659265</td>\n",
" <td>0.194147</td>\n",
" <td>0.070442</td>\n",
" <td>0.058954</td>\n",
" <td>-0.005558</td>\n",
" <td>0.010090</td>\n",
" <td>0.060069</td>\n",
" </tr>\n",
" <tr>\n",
" <th>371</th>\n",
" <td>1970-01-01 00:42:07.965357000</td>\n",
" <td>9831.0</td>\n",
" <td>10486.0</td>\n",
" <td>2.933909</td>\n",
" <td>-0.276617</td>\n",
" <td>0.502136</td>\n",
" <td>0.053958</td>\n",
" <td>-0.001683</td>\n",
" <td>0.011314</td>\n",
" <td>0.055157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>372</th>\n",
" <td>1970-01-01 00:42:07.986261999</td>\n",
" <td>9831.0</td>\n",
" <td>10486.0</td>\n",
" <td>2.581123</td>\n",
" <td>-0.080497</td>\n",
" <td>0.541207</td>\n",
" <td>0.054906</td>\n",
" <td>0.007043</td>\n",
" <td>0.002988</td>\n",
" <td>0.055437</td>\n",
" </tr>\n",
" <tr>\n",
" <th>375</th>\n",
" <td>1970-01-01 00:42:08.027263999</td>\n",
" <td>9831.0</td>\n",
" <td>10486.0</td>\n",
" <td>3.169483</td>\n",
" <td>0.076552</td>\n",
" <td>0.227492</td>\n",
" <td>0.057236</td>\n",
" <td>-0.000823</td>\n",
" <td>-0.003302</td>\n",
" <td>0.057337</td>\n",
" </tr>\n",
" <tr>\n",
" <th>376</th>\n",
" <td>1970-01-01 00:42:08.047309000</td>\n",
" <td>9831.0</td>\n",
" <td>10486.0</td>\n",
" <td>2.855385</td>\n",
" <td>-0.041043</td>\n",
" <td>-0.164748</td>\n",
" <td>0.050789</td>\n",
" <td>-0.001687</td>\n",
" <td>-0.010025</td>\n",
" <td>0.051797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>377</th>\n",
" <td>1970-01-01 00:42:08.068260999</td>\n",
" <td>9831.0</td>\n",
" <td>10486.0</td>\n",
" <td>2.424074</td>\n",
" <td>-0.080497</td>\n",
" <td>-0.478464</td>\n",
" <td>0.049393</td>\n",
" <td>-0.003972</td>\n",
" <td>-0.005662</td>\n",
" <td>0.049875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>381</th>\n",
" <td>1970-01-01 00:42:08.109296000</td>\n",
" <td>9831.0</td>\n",
" <td>9503.0</td>\n",
" <td>2.777243</td>\n",
" <td>0.272672</td>\n",
" <td>0.306016</td>\n",
" <td>0.051710</td>\n",
" <td>-0.001613</td>\n",
" <td>0.016345</td>\n",
" <td>0.054256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>389</th>\n",
" <td>1970-01-01 00:42:08.191333000</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>2.267025</td>\n",
" <td>-0.355142</td>\n",
" <td>-0.439393</td>\n",
" <td>-0.018945</td>\n",
" <td>-0.005759</td>\n",
" <td>-0.040177</td>\n",
" <td>0.044791</td>\n",
" </tr>\n",
" <tr>\n",
" <th>390</th>\n",
" <td>1970-01-01 00:42:08.212152000</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>-0.909966</td>\n",
" <td>-0.276617</td>\n",
" <td>-1.929828</td>\n",
" <td>-0.067803</td>\n",
" <td>-0.013413</td>\n",
" <td>-0.023752</td>\n",
" <td>0.073084</td>\n",
" </tr>\n",
" <tr>\n",
" <th>391</th>\n",
" <td>1970-01-01 00:42:08.232206000</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>-3.381002</td>\n",
" <td>-0.668857</td>\n",
" <td>-1.184419</td>\n",
" <td>0.016322</td>\n",
" <td>-0.006314</td>\n",
" <td>0.021023</td>\n",
" <td>0.027354</td>\n",
" </tr>\n",
" <tr>\n",
" <th>392</th>\n",
" <td>1970-01-01 00:42:08.252207999</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.816043</td>\n",
" <td>-0.315688</td>\n",
" <td>1.051042</td>\n",
" <td>-0.000165</td>\n",
" <td>0.008179</td>\n",
" <td>0.002303</td>\n",
" <td>0.008499</td>\n",
" </tr>\n",
" <tr>\n",
" <th>393</th>\n",
" <td>1970-01-01 00:42:08.273165000</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>-0.007891</td>\n",
" <td>0.390267</td>\n",
" <td>0.109896</td>\n",
" <td>-0.009585</td>\n",
" <td>0.004670</td>\n",
" <td>0.003773</td>\n",
" <td>0.011310</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",
" </tr>\n",
" <tr>\n",
" <th>527</th>\n",
" <td>1970-01-01 00:42:10.981529000</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>-0.007891</td>\n",
" <td>0.155077</td>\n",
" <td>-0.164748</td>\n",
" <td>0.002303</td>\n",
" <td>0.005724</td>\n",
" <td>-0.000990</td>\n",
" <td>0.006248</td>\n",
" </tr>\n",
" <tr>\n",
" <th>528</th>\n",
" <td>1970-01-01 00:42:11.002520000</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.109705</td>\n",
" <td>0.272672</td>\n",
" <td>-0.047153</td>\n",
" <td>0.003774</td>\n",
" <td>0.003892</td>\n",
" <td>0.002986</td>\n",
" <td>0.006189</td>\n",
" </tr>\n",
" <tr>\n",
" <th>529</th>\n",
" <td>1970-01-01 00:42:11.022567999</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.188229</td>\n",
" <td>0.194147</td>\n",
" <td>0.148967</td>\n",
" <td>0.003918</td>\n",
" <td>0.001593</td>\n",
" <td>0.003101</td>\n",
" <td>0.005244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>530</th>\n",
" <td>1970-01-01 00:42:11.043383000</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.188229</td>\n",
" <td>0.076552</td>\n",
" <td>0.148967</td>\n",
" <td>0.002988</td>\n",
" <td>0.001534</td>\n",
" <td>0.001411</td>\n",
" <td>0.003643</td>\n",
" </tr>\n",
" <tr>\n",
" <th>531</th>\n",
" <td>1970-01-01 00:42:11.063418000</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.149158</td>\n",
" <td>0.076552</td>\n",
" <td>0.070442</td>\n",
" <td>0.001481</td>\n",
" <td>0.002424</td>\n",
" <td>-0.003453</td>\n",
" <td>0.004471</td>\n",
" </tr>\n",
" <tr>\n",
" <th>532</th>\n",
" <td>1970-01-01 00:42:11.084379999</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.070634</td>\n",
" <td>0.115623</td>\n",
" <td>-0.164748</td>\n",
" <td>0.003772</td>\n",
" <td>0.003107</td>\n",
" <td>-0.001728</td>\n",
" <td>0.005183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>533</th>\n",
" <td>1970-01-01 00:42:11.104417999</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.188229</td>\n",
" <td>0.155077</td>\n",
" <td>-0.086224</td>\n",
" <td>0.003132</td>\n",
" <td>0.003256</td>\n",
" <td>0.003956</td>\n",
" <td>0.006005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>534</th>\n",
" <td>1970-01-01 00:42:11.125413000</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.149158</td>\n",
" <td>0.155077</td>\n",
" <td>0.188421</td>\n",
" <td>0.002985</td>\n",
" <td>0.001532</td>\n",
" <td>0.005335</td>\n",
" <td>0.006303</td>\n",
" </tr>\n",
" <tr>\n",
" <th>535</th>\n",
" <td>1970-01-01 00:42:11.145427999</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.149158</td>\n",
" <td>0.076552</td>\n",
" <td>0.266562</td>\n",
" <td>0.003127</td>\n",
" <td>0.001605</td>\n",
" <td>-0.000989</td>\n",
" <td>0.003651</td>\n",
" </tr>\n",
" <tr>\n",
" <th>536</th>\n",
" <td>1970-01-01 00:42:11.166393999</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.149158</td>\n",
" <td>0.076552</td>\n",
" <td>-0.047153</td>\n",
" <td>-0.000158</td>\n",
" <td>0.002316</td>\n",
" <td>-0.004083</td>\n",
" <td>0.004697</td>\n",
" </tr>\n",
" <tr>\n",
" <th>539</th>\n",
" <td>1970-01-01 00:42:11.207393000</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.070634</td>\n",
" <td>0.155077</td>\n",
" <td>-0.086224</td>\n",
" <td>0.003948</td>\n",
" <td>0.002425</td>\n",
" <td>0.003124</td>\n",
" <td>0.005588</td>\n",
" </tr>\n",
" <tr>\n",
" <th>540</th>\n",
" <td>1970-01-01 00:42:11.228365999</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.188229</td>\n",
" <td>0.115623</td>\n",
" <td>0.148967</td>\n",
" <td>0.003772</td>\n",
" <td>0.002317</td>\n",
" <td>0.003776</td>\n",
" <td>0.005818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>541</th>\n",
" <td>1970-01-01 00:42:11.248403999</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.188229</td>\n",
" <td>0.115623</td>\n",
" <td>0.188421</td>\n",
" <td>0.002299</td>\n",
" <td>0.000778</td>\n",
" <td>-0.000988</td>\n",
" <td>0.002621</td>\n",
" </tr>\n",
" <tr>\n",
" <th>542</th>\n",
" <td>1970-01-01 00:42:11.269362999</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.109705</td>\n",
" <td>0.037098</td>\n",
" <td>-0.047153</td>\n",
" <td>0.001416</td>\n",
" <td>0.003109</td>\n",
" <td>-0.003303</td>\n",
" <td>0.004753</td>\n",
" </tr>\n",
" <tr>\n",
" <th>543</th>\n",
" <td>1970-01-01 00:42:11.289413999</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.070634</td>\n",
" <td>0.155077</td>\n",
" <td>-0.164748</td>\n",
" <td>0.003127</td>\n",
" <td>0.004070</td>\n",
" <td>-0.000988</td>\n",
" <td>0.005226</td>\n",
" </tr>\n",
" <tr>\n",
" <th>544</th>\n",
" <td>1970-01-01 00:42:11.310375999</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.149158</td>\n",
" <td>0.194147</td>\n",
" <td>-0.047153</td>\n",
" <td>0.004566</td>\n",
" <td>0.003893</td>\n",
" <td>0.002987</td>\n",
" <td>0.006703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>545</th>\n",
" <td>1970-01-01 00:42:11.330429000</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.227683</td>\n",
" <td>0.194147</td>\n",
" <td>0.148967</td>\n",
" <td>0.003943</td>\n",
" <td>0.001604</td>\n",
" <td>0.003947</td>\n",
" <td>0.005806</td>\n",
" </tr>\n",
" <tr>\n",
" <th>546</th>\n",
" <td>1970-01-01 00:42:11.351379000</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.188229</td>\n",
" <td>0.076552</td>\n",
" <td>0.188421</td>\n",
" <td>0.002989</td>\n",
" <td>0.001534</td>\n",
" <td>-0.000154</td>\n",
" <td>0.003363</td>\n",
" </tr>\n",
" <tr>\n",
" <th>547</th>\n",
" <td>1970-01-01 00:42:11.371416999</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.149158</td>\n",
" <td>0.076552</td>\n",
" <td>-0.007699</td>\n",
" <td>0.000662</td>\n",
" <td>0.004071</td>\n",
" <td>-0.003455</td>\n",
" <td>0.005381</td>\n",
" </tr>\n",
" <tr>\n",
" <th>548</th>\n",
" <td>1970-01-01 00:42:11.392388000</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.031563</td>\n",
" <td>0.194147</td>\n",
" <td>-0.164748</td>\n",
" <td>0.002198</td>\n",
" <td>0.004674</td>\n",
" <td>-0.000154</td>\n",
" <td>0.005167</td>\n",
" </tr>\n",
" <tr>\n",
" <th>549</th>\n",
" <td>1970-01-01 00:42:11.412428000</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.109705</td>\n",
" <td>0.233218</td>\n",
" <td>-0.007699</td>\n",
" <td>0.003943</td>\n",
" <td>0.002422</td>\n",
" <td>0.006411</td>\n",
" <td>0.007907</td>\n",
" </tr>\n",
" <tr>\n",
" <th>550</th>\n",
" <td>1970-01-01 00:42:11.433378000</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.188229</td>\n",
" <td>0.115623</td>\n",
" <td>0.306016</td>\n",
" <td>0.002989</td>\n",
" <td>0.001534</td>\n",
" <td>0.002985</td>\n",
" <td>0.004494</td>\n",
" </tr>\n",
" <tr>\n",
" <th>551</th>\n",
" <td>1970-01-01 00:42:11.453414000</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.149158</td>\n",
" <td>0.076552</td>\n",
" <td>0.148967</td>\n",
" <td>0.002300</td>\n",
" <td>0.003251</td>\n",
" <td>-0.001808</td>\n",
" <td>0.004373</td>\n",
" </tr>\n",
" <tr>\n",
" <th>552</th>\n",
" <td>1970-01-01 00:42:11.474377999</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.109705</td>\n",
" <td>0.155077</td>\n",
" <td>-0.086224</td>\n",
" <td>0.001473</td>\n",
" <td>0.004049</td>\n",
" <td>-0.001798</td>\n",
" <td>0.004669</td>\n",
" </tr>\n",
" <tr>\n",
" <th>553</th>\n",
" <td>1970-01-01 00:42:11.495234999</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.070634</td>\n",
" <td>0.194147</td>\n",
" <td>-0.086224</td>\n",
" <td>0.004560</td>\n",
" <td>0.003106</td>\n",
" <td>0.003774</td>\n",
" <td>0.006685</td>\n",
" </tr>\n",
" <tr>\n",
" <th>554</th>\n",
" <td>1970-01-01 00:42:11.515264000</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.227683</td>\n",
" <td>0.155077</td>\n",
" <td>0.188421</td>\n",
" <td>0.004562</td>\n",
" <td>0.001534</td>\n",
" <td>0.002202</td>\n",
" <td>0.005293</td>\n",
" </tr>\n",
" <tr>\n",
" <th>555</th>\n",
" <td>1970-01-01 00:42:11.535301999</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.227683</td>\n",
" <td>0.076552</td>\n",
" <td>0.109896</td>\n",
" <td>0.003127</td>\n",
" <td>0.003251</td>\n",
" <td>-0.000988</td>\n",
" <td>0.004617</td>\n",
" </tr>\n",
" <tr>\n",
" <th>556</th>\n",
" <td>1970-01-01 00:42:11.556263999</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.149158</td>\n",
" <td>0.155077</td>\n",
" <td>-0.047153</td>\n",
" <td>0.002301</td>\n",
" <td>0.003252</td>\n",
" <td>-0.005102</td>\n",
" <td>0.006473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>557</th>\n",
" <td>1970-01-01 00:42:11.577236000</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.109705</td>\n",
" <td>0.155077</td>\n",
" <td>-0.243273</td>\n",
" <td>0.002991</td>\n",
" <td>0.003893</td>\n",
" <td>0.000629</td>\n",
" <td>0.004950</td>\n",
" </tr>\n",
" <tr>\n",
" <th>558</th>\n",
" <td>1970-01-01 00:42:11.597288999</td>\n",
" <td>9831.0</td>\n",
" <td>9831.0</td>\n",
" <td>0.149158</td>\n",
" <td>0.194147</td>\n",
" <td>0.031372</td>\n",
" <td>-0.000000</td>\n",
" <td>-0.000000</td>\n",
" <td>-0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>189 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 drive_pwm__pwm_angle drive_pwm__pwm_drive \\\n",
"352 1970-01-01 00:42:07.575413999 9831.0 10486.0 \n",
"353 1970-01-01 00:42:07.595447000 9831.0 10486.0 \n",
"354 1970-01-01 00:42:07.616406999 9831.0 10486.0 \n",
"355 1970-01-01 00:42:07.636457999 9831.0 10486.0 \n",
"356 1970-01-01 00:42:07.657416999 9831.0 10486.0 \n",
"357 1970-01-01 00:42:07.678405000 9831.0 10486.0 \n",
"358 1970-01-01 00:42:07.698432000 9831.0 10486.0 \n",
"359 1970-01-01 00:42:07.719416999 9831.0 10486.0 \n",
"360 1970-01-01 00:42:07.739416999 9831.0 10486.0 \n",
"361 1970-01-01 00:42:07.760403000 9831.0 10486.0 \n",
"362 1970-01-01 00:42:07.780419000 9831.0 10486.0 \n",
"363 1970-01-01 00:42:07.801277999 9831.0 10486.0 \n",
"364 1970-01-01 00:42:07.821316000 9831.0 10486.0 \n",
"365 1970-01-01 00:42:07.842277000 9831.0 10486.0 \n",
"366 1970-01-01 00:42:07.862316000 9831.0 10486.0 \n",
"367 1970-01-01 00:42:07.883291000 9831.0 10486.0 \n",
"368 1970-01-01 00:42:07.903315999 9831.0 10486.0 \n",
"369 1970-01-01 00:42:07.924277999 9831.0 10486.0 \n",
"370 1970-01-01 00:42:07.945263000 9831.0 10486.0 \n",
"371 1970-01-01 00:42:07.965357000 9831.0 10486.0 \n",
"372 1970-01-01 00:42:07.986261999 9831.0 10486.0 \n",
"375 1970-01-01 00:42:08.027263999 9831.0 10486.0 \n",
"376 1970-01-01 00:42:08.047309000 9831.0 10486.0 \n",
"377 1970-01-01 00:42:08.068260999 9831.0 10486.0 \n",
"381 1970-01-01 00:42:08.109296000 9831.0 9503.0 \n",
"389 1970-01-01 00:42:08.191333000 9831.0 9831.0 \n",
"390 1970-01-01 00:42:08.212152000 9831.0 9831.0 \n",
"391 1970-01-01 00:42:08.232206000 9831.0 9831.0 \n",
"392 1970-01-01 00:42:08.252207999 9831.0 9831.0 \n",
"393 1970-01-01 00:42:08.273165000 9831.0 9831.0 \n",
".. ... ... ... \n",
"527 1970-01-01 00:42:10.981529000 9831.0 9831.0 \n",
"528 1970-01-01 00:42:11.002520000 9831.0 9831.0 \n",
"529 1970-01-01 00:42:11.022567999 9831.0 9831.0 \n",
"530 1970-01-01 00:42:11.043383000 9831.0 9831.0 \n",
"531 1970-01-01 00:42:11.063418000 9831.0 9831.0 \n",
"532 1970-01-01 00:42:11.084379999 9831.0 9831.0 \n",
"533 1970-01-01 00:42:11.104417999 9831.0 9831.0 \n",
"534 1970-01-01 00:42:11.125413000 9831.0 9831.0 \n",
"535 1970-01-01 00:42:11.145427999 9831.0 9831.0 \n",
"536 1970-01-01 00:42:11.166393999 9831.0 9831.0 \n",
"539 1970-01-01 00:42:11.207393000 9831.0 9831.0 \n",
"540 1970-01-01 00:42:11.228365999 9831.0 9831.0 \n",
"541 1970-01-01 00:42:11.248403999 9831.0 9831.0 \n",
"542 1970-01-01 00:42:11.269362999 9831.0 9831.0 \n",
"543 1970-01-01 00:42:11.289413999 9831.0 9831.0 \n",
"544 1970-01-01 00:42:11.310375999 9831.0 9831.0 \n",
"545 1970-01-01 00:42:11.330429000 9831.0 9831.0 \n",
"546 1970-01-01 00:42:11.351379000 9831.0 9831.0 \n",
"547 1970-01-01 00:42:11.371416999 9831.0 9831.0 \n",
"548 1970-01-01 00:42:11.392388000 9831.0 9831.0 \n",
"549 1970-01-01 00:42:11.412428000 9831.0 9831.0 \n",
"550 1970-01-01 00:42:11.433378000 9831.0 9831.0 \n",
"551 1970-01-01 00:42:11.453414000 9831.0 9831.0 \n",
"552 1970-01-01 00:42:11.474377999 9831.0 9831.0 \n",
"553 1970-01-01 00:42:11.495234999 9831.0 9831.0 \n",
"554 1970-01-01 00:42:11.515264000 9831.0 9831.0 \n",
"555 1970-01-01 00:42:11.535301999 9831.0 9831.0 \n",
"556 1970-01-01 00:42:11.556263999 9831.0 9831.0 \n",
"557 1970-01-01 00:42:11.577236000 9831.0 9831.0 \n",
"558 1970-01-01 00:42:11.597288999 9831.0 9831.0 \n",
"\n",
" imu__linear_acceleration_x imu__linear_acceleration_y \\\n",
"352 0.070634 0.037098 \n",
"353 0.031563 -0.001973 \n",
"354 -0.007891 -0.041043 \n",
"355 -0.086415 0.076552 \n",
"356 -0.007891 0.037098 \n",
"357 0.501945 -1.531862 \n",
"358 10.778709 0.586387 \n",
"359 6.189425 -0.864977 \n",
"360 -0.321606 -0.394213 \n",
"361 3.522269 0.507863 \n",
"362 -0.988491 0.468792 \n",
"363 2.502598 0.272672 \n",
"364 1.835714 -0.119568 \n",
"365 1.874785 -0.080497 \n",
"366 2.188883 -0.198093 \n",
"367 2.110358 -0.472737 \n",
"368 1.718118 -0.001973 \n",
"369 2.385003 -0.041043 \n",
"370 2.659265 0.194147 \n",
"371 2.933909 -0.276617 \n",
"372 2.581123 -0.080497 \n",
"375 3.169483 0.076552 \n",
"376 2.855385 -0.041043 \n",
"377 2.424074 -0.080497 \n",
"381 2.777243 0.272672 \n",
"389 2.267025 -0.355142 \n",
"390 -0.909966 -0.276617 \n",
"391 -3.381002 -0.668857 \n",
"392 0.816043 -0.315688 \n",
"393 -0.007891 0.390267 \n",
".. ... ... \n",
"527 -0.007891 0.155077 \n",
"528 0.109705 0.272672 \n",
"529 0.188229 0.194147 \n",
"530 0.188229 0.076552 \n",
"531 0.149158 0.076552 \n",
"532 0.070634 0.115623 \n",
"533 0.188229 0.155077 \n",
"534 0.149158 0.155077 \n",
"535 0.149158 0.076552 \n",
"536 0.149158 0.076552 \n",
"539 0.070634 0.155077 \n",
"540 0.188229 0.115623 \n",
"541 0.188229 0.115623 \n",
"542 0.109705 0.037098 \n",
"543 0.070634 0.155077 \n",
"544 0.149158 0.194147 \n",
"545 0.227683 0.194147 \n",
"546 0.188229 0.076552 \n",
"547 0.149158 0.076552 \n",
"548 0.031563 0.194147 \n",
"549 0.109705 0.233218 \n",
"550 0.188229 0.115623 \n",
"551 0.149158 0.076552 \n",
"552 0.109705 0.155077 \n",
"553 0.070634 0.194147 \n",
"554 0.227683 0.155077 \n",
"555 0.227683 0.076552 \n",
"556 0.149158 0.155077 \n",
"557 0.109705 0.155077 \n",
"558 0.149158 0.194147 \n",
"\n",
" imu__linear_acceleration_z instant_velocity_x instant_velocity_y \\\n",
"352 0.227492 0.000632 -0.000040 \n",
"353 0.188421 -0.000165 -0.000860 \n",
"354 -0.047153 -0.001733 0.001535 \n",
"355 -0.125678 -0.000165 0.000778 \n",
"356 -0.007699 0.010535 -0.032151 \n",
"357 1.404212 0.215865 0.011744 \n",
"358 4.973442 0.129885 -0.018152 \n",
"359 -1.929828 -0.006432 -0.007884 \n",
"360 -2.753379 0.073918 0.010658 \n",
"361 1.953501 -0.019786 0.009383 \n",
"362 -1.890758 0.052202 0.005688 \n",
"363 0.266562 0.036784 -0.002396 \n",
"364 1.129567 0.039297 -0.001687 \n",
"365 0.423612 0.043863 -0.003970 \n",
"366 -0.478464 0.044265 -0.009916 \n",
"367 -0.125678 0.034405 -0.000040 \n",
"368 -0.047153 0.049994 -0.000860 \n",
"369 0.541207 0.055805 0.004074 \n",
"370 0.070442 0.058954 -0.005558 \n",
"371 0.502136 0.053958 -0.001683 \n",
"372 0.541207 0.054906 0.007043 \n",
"375 0.227492 0.057236 -0.000823 \n",
"376 -0.164748 0.050789 -0.001687 \n",
"377 -0.478464 0.049393 -0.003972 \n",
"381 0.306016 0.051710 -0.001613 \n",
"389 -0.439393 -0.018945 -0.005759 \n",
"390 -1.929828 -0.067803 -0.013413 \n",
"391 -1.184419 0.016322 -0.006314 \n",
"392 1.051042 -0.000165 0.008179 \n",
"393 0.109896 -0.009585 0.004670 \n",
".. ... ... ... \n",
"527 -0.164748 0.002303 0.005724 \n",
"528 -0.047153 0.003774 0.003892 \n",
"529 0.148967 0.003918 0.001593 \n",
"530 0.148967 0.002988 0.001534 \n",
"531 0.070442 0.001481 0.002424 \n",
"532 -0.164748 0.003772 0.003107 \n",
"533 -0.086224 0.003132 0.003256 \n",
"534 0.188421 0.002985 0.001532 \n",
"535 0.266562 0.003127 0.001605 \n",
"536 -0.047153 -0.000158 0.002316 \n",
"539 -0.086224 0.003948 0.002425 \n",
"540 0.148967 0.003772 0.002317 \n",
"541 0.188421 0.002299 0.000778 \n",
"542 -0.047153 0.001416 0.003109 \n",
"543 -0.164748 0.003127 0.004070 \n",
"544 -0.047153 0.004566 0.003893 \n",
"545 0.148967 0.003943 0.001604 \n",
"546 0.188421 0.002989 0.001534 \n",
"547 -0.007699 0.000662 0.004071 \n",
"548 -0.164748 0.002198 0.004674 \n",
"549 -0.007699 0.003943 0.002422 \n",
"550 0.306016 0.002989 0.001534 \n",
"551 0.148967 0.002300 0.003251 \n",
"552 -0.086224 0.001473 0.004049 \n",
"553 -0.086224 0.004560 0.003106 \n",
"554 0.188421 0.004562 0.001534 \n",
"555 0.109896 0.003127 0.003251 \n",
"556 -0.047153 0.002301 0.003252 \n",
"557 -0.243273 0.002991 0.003893 \n",
"558 0.031372 -0.000000 -0.000000 \n",
"\n",
" instant_velocity_z speed \n",
"352 0.003775 0.003827 \n",
"353 -0.000988 0.001321 \n",
"354 -0.002520 0.003422 \n",
"355 -0.000161 0.000811 \n",
"356 0.029472 0.044869 \n",
"357 0.099603 0.238026 \n",
"358 -0.040497 0.137258 \n",
"359 -0.055068 0.056000 \n",
"360 0.040996 0.085195 \n",
"361 -0.037845 0.043724 \n",
"362 0.005560 0.052804 \n",
"363 0.022634 0.043256 \n",
"364 0.008879 0.040323 \n",
"365 -0.009588 0.045074 \n",
"366 -0.002636 0.045438 \n",
"367 -0.000944 0.034418 \n",
"368 0.011345 0.051273 \n",
"369 0.001478 0.055973 \n",
"370 0.010090 0.060069 \n",
"371 0.011314 0.055157 \n",
"372 0.002988 0.055437 \n",
"375 -0.003302 0.057337 \n",
"376 -0.010025 0.051797 \n",
"377 -0.005662 0.049875 \n",
"381 0.016345 0.054256 \n",
"389 -0.040177 0.044791 \n",
"390 -0.023752 0.073084 \n",
"391 0.021023 0.027354 \n",
"392 0.002303 0.008499 \n",
"393 0.003773 0.011310 \n",
".. ... ... \n",
"527 -0.000990 0.006248 \n",
"528 0.002986 0.006189 \n",
"529 0.003101 0.005244 \n",
"530 0.001411 0.003643 \n",
"531 -0.003453 0.004471 \n",
"532 -0.001728 0.005183 \n",
"533 0.003956 0.006005 \n",
"534 0.005335 0.006303 \n",
"535 -0.000989 0.003651 \n",
"536 -0.004083 0.004697 \n",
"539 0.003124 0.005588 \n",
"540 0.003776 0.005818 \n",
"541 -0.000988 0.002621 \n",
"542 -0.003303 0.004753 \n",
"543 -0.000988 0.005226 \n",
"544 0.002987 0.006703 \n",
"545 0.003947 0.005806 \n",
"546 -0.000154 0.003363 \n",
"547 -0.003455 0.005381 \n",
"548 -0.000154 0.005167 \n",
"549 0.006411 0.007907 \n",
"550 0.002985 0.004494 \n",
"551 -0.001808 0.004373 \n",
"552 -0.001798 0.004669 \n",
"553 0.003774 0.006685 \n",
"554 0.002202 0.005293 \n",
"555 -0.000988 0.004617 \n",
"556 -0.005102 0.006473 \n",
"557 0.000629 0.004950 \n",
"558 -0.000000 0.000000 \n",
"\n",
"[189 rows x 10 columns]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# df = pd.DataFrame({\"A\": [1, 2, 3], \"B\": [4, 5, 6]})\n",
"# df.rename(columns={\"A\": \"a\"})\n",
"pwm_for_correlation.rename(columns={0: 'timething'}, inplace=True)\n",
"pwm_for_correlation"
]
}
],
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}