{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Mockup Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To test the `FeatureExtractor` class from the `time_domain_features.py` script with a simple mockup dataset of 5 to 10 data points directly in a Python notebook."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Importing Modules\n",
"\n",
"Use relative imports or modify the path to include the directory where the module is stored. In this example, we’ll simulate the relative import setup."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import numpy as np\n",
"import pandas as pd\n",
"# Assuming the src directory is one level up from the notebooks directory\n",
"sys.path.append('../src/features')\n",
"\n",
"from time_domain_features import FeatureExtractor"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Mockup Data\n",
"\n",
"Create a small dataset with 5 to 10 data points. Simulate importing the `FeatureExtractor` from its relative path in the notebooks directory."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" SampleData \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 0.496714 \n",
" \n",
" \n",
" 1 \n",
" -0.138264 \n",
" \n",
" \n",
" 2 \n",
" 0.647689 \n",
" \n",
" \n",
" 3 \n",
" 1.523030 \n",
" \n",
" \n",
" 4 \n",
" -0.234153 \n",
" \n",
" \n",
" 5 \n",
" -0.234137 \n",
" \n",
" \n",
" 6 \n",
" 1.579213 \n",
" \n",
" \n",
" 7 \n",
" 0.767435 \n",
" \n",
" \n",
" 8 \n",
" -0.469474 \n",
" \n",
" \n",
" 9 \n",
" 0.542560 \n",
" \n",
" \n",
"
\n",
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# Plotting the data points\n",
"plt.figure(figsize=(8, 6))\n",
"plt.plot(mock_df.index, mock_df['SampleData'], marker='o', color='blue', label='Data Points')\n",
"plt.title('Scatter Plot of Data Points')\n",
"plt.xlabel('Time')\n",
"plt.ylabel('SampleData')\n",
"plt.legend()\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Print Time-domain Features"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" Mean \n",
" Max \n",
" Peak (Pm) \n",
" Peak-to-Peak (Pk) \n",
" RMS \n",
" Variance \n",
" Standard Deviation \n",
" Power \n",
" Crest Factor \n",
" Form Factor \n",
" Pulse Indicator \n",
" Margin \n",
" Kurtosis \n",
" Skewness \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 0.448061 \n",
" 1.579213 \n",
" 1.579213 \n",
" 2.048687 \n",
" 0.819284 \n",
" 0.470467 \n",
" 0.723008 \n",
" 0.671226 \n",
" 1.927553 \n",
" 1.828509 \n",
" 3.524548 \n",
" 2.067638 \n",
" 1.917716 \n",
" 0.412307 \n",
" \n",
" \n",
"
\n",
"