{
"cells": [
{
"cell_type": "markdown",
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"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": {
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"\n",
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\n",
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" SampleData \n",
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" 0 \n",
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" 4 \n",
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" 5 \n",
" -0.234137 \n",
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" 6 \n",
" 1.579213 \n",
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\n",
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},
"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()"
]
}
],
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