813 lines
22 KiB
Plaintext
813 lines
22 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Mockup Data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Importing Modules\n",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Create Mockup Data\n",
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"\n",
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"Create a small dataset with 5 to 10 data points. Simulate importing the `FeatureExtractor` from its relative path in the notebooks directory."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create mockup data\n",
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"np.random.seed(42) # For reproducibility\n",
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"mock_data = np.random.randn(10) # Generate 10 random data points\n",
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"\n",
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"# Convert to DataFrame (simulating processed data input)\n",
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"mock_df = pd.DataFrame(mock_data, columns=['SampleData'])\n",
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"mock_df"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Visualize Data Points"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Plotting the data points\n",
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"plt.figure(figsize=(8, 6))\n",
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"plt.plot(mock_df.index, mock_df['SampleData'], marker='o', color='blue', label='Data Points')\n",
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"plt.title('Scatter Plot of Data Points')\n",
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"plt.xlabel('Time')\n",
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"plt.ylabel('SampleData')\n",
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"plt.legend()\n",
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"plt.grid(True)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Print Time-domain Features (Single Mockup Data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import sys\n",
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"import os\n",
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"# Assuming the src directory is one level up from the notebooks directory\n",
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"sys.path.append('../src/features')\n",
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"from time_domain_features import FeatureExtractor\n",
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"\n",
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"\n",
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"# Extract features\n",
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"extracted = FeatureExtractor(mock_df['SampleData'])\n",
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"\n",
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"# Format with pandas DataFramw\n",
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"features = pd.DataFrame(extracted.features, index=[0])\n",
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"features\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Print Time-domain Features (Multiple CSV Mockup Data)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Importing modules"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import sys\n",
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"import os\n",
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"import re\n",
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"# Assuming the src directory is one level up from the notebooks directory\n",
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"sys.path.append('../src/features')\n",
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"from time_domain_features import ExtractTimeFeatures # use wrapper function instead of class for easy use\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### The function"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Define a function to extract numbers from a filename that later used as labels features\n",
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"def extract_numbers(filename):\n",
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" '''\n",
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" Extract numbers from a filename\n",
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"\n",
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" Parameters\n",
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" ----------\n",
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" filename : str\n",
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" The filename to extract numbers from\n",
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"\n",
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" Returns\n",
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" -------\n",
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" list\n",
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" A list of extracted numbers: [damage_number, test_number, sensor_number]\n",
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" '''\n",
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" # Find all occurrences of one or more digits in the filename\n",
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" numbers = re.findall(r'\\d+', filename)\n",
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" # Convert the list of number strings to integers\n",
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" numbers = [int(num) for num in numbers]\n",
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" # Convert to a tuple and return\n",
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" return numbers\n",
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"\n",
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"def build_features(input_dir:str, sensor:int=None):\n",
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" all_features = []\n",
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" for nth_damage in os.listdir(input_dir):\n",
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" nth_damage_path = os.path.join(input_dir, nth_damage)\n",
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" print(f'Extracting features from damage folder {nth_damage_path}')\n",
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" if os.path.isdir(nth_damage_path):\n",
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" for nth_test in os.listdir(nth_damage_path):\n",
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" nth_test_path = os.path.join(nth_damage_path, nth_test)\n",
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" if sensor is not None:\n",
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" # Check if the file has the specified sensor suffix\n",
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" if not nth_test.endswith(f'_{sensor}.csv'):\n",
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" continue\n",
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" features = ExtractTimeFeatures(nth_test_path) # return the one csv file feature in dictionary {}\n",
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" features['label'] = extract_numbers(nth_test)[0] # add labels to the dictionary\n",
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" features['filename'] = nth_test # add filename to the dictionary\n",
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" all_features.append(features)\n",
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"\n",
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" # Create a DataFrame from the list of dictionaries\n",
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" df = pd.DataFrame(all_features)\n",
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" return df"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Execute the automation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data_dir = \"../../data/raw\"\n",
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"# Extract features\n",
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"df1 = build_features(data_dir, sensor=1)\n",
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"df2 = build_features(data_dir, sensor=2)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##### Sensor 1 Extracted Features"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df1.head(5)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##### Sensor 2 Extracted Features"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df2.head(5)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##### Perform division"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Separate the label column\n",
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"label_column = df1.iloc[:, -2]\n",
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"\n",
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"# Perform the relative value by operate division on all the features\n",
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"df_relative = df2.iloc[:, :-2] / df1.iloc[:, :-2]\n",
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"\n",
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"# Add the label column back to the resulting DataFrame\n",
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"df_relative['label'] = label_column\n",
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"\n",
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"# Append a string to all column names\n",
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"suffix = '_rel'\n",
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"df_relative.columns = [col + suffix if col != 'label' else col for col in df_relative.columns]\n",
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"\n",
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"# Display the first 5 rows of the resulting DataFrame\n",
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"df_relative\n",
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"df_relative.info()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Subsetting DataFrame to see the pair plots due to many features"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Assuming your DataFrame is named 'df'\n",
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"\n",
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"# Subsetting the DataFrame to include only the first 3 columns and the label\n",
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"subset_df = df_relative[['Mean_rel', 'Max_rel', 'Peak (Pm)_rel', 'label']]\n",
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"\n",
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"# Plotting the pairplot\n",
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"g = sns.pairplot(subset_df, hue='label', diag_kind='kde')\n",
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"\n",
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"# Adjusting the axis limits\n",
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"for ax in g.axes.flatten():\n",
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" ax.set_xlim(-10, 10) # Adjust these limits based on your data\n",
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" ax.set_ylim(-10, 10) # Adjust these limits based on your data\n",
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"\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##### Standard Scaler"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.preprocessing import StandardScaler\n",
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"\n",
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"# Assuming 'subset_df' is your DataFrame\n",
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"features = subset_df.columns[:-1] # Select all columns except the label\n",
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"scaler = StandardScaler()\n",
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"subset_df[features] = scaler.fit_transform(subset_df[features])\n",
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"\n",
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"# Plotting the pairplot\n",
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"sns.pairplot(subset_df, hue='label', diag_kind='kde')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##### Min-Max Scaler"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.preprocessing import MinMaxScaler\n",
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"\n",
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"# Assuming 'subset_df' is your DataFrame\n",
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"features = subset_df.columns[:-1] # Select all columns except the label\n",
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"scaler = MinMaxScaler()\n",
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"subset_df[features] = scaler.fit_transform(subset_df[features])\n",
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"\n",
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"# Plotting the pairplot\n",
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"sns.pairplot(subset_df, hue='label', diag_kind='kde')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## QUGS Data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To test the `FeatureExtractor` class from the `time_domain_features.py` script with real data from QUGS that has been converted purposed for the thesis."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Importing Modules\n",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Create Real DataFrame\n",
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"\n",
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"Create one DataFrame from one of the raw data file. Simulate importing the `FeatureExtractor` from its relative path in the notebooks directory."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Convert to DataFrame (simulating processed data input)\n",
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"single_data_dir = \"D:/thesis/data/converted/raw/DAMAGE_2/D2_TEST05_01.csv\"\n",
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"df = pd.read_csv(single_data_dir)\n",
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##### Absolute the data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df[df.columns[-1]] = df[df.columns[-1]].abs()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Visualize Data Points"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Plotting the data points\n",
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"plt.figure(figsize=(8, 6))\n",
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"plt.plot(df['Time'], df[df.columns[-1]], marker='o', color='blue', label='Data Points')\n",
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"plt.title('Scatter Plot of Data Points')\n",
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"plt.xlabel('Time')\n",
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"plt.ylabel('Amp')\n",
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"plt.legend()\n",
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"plt.grid(True)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Downsampled Plot with Alpha Blending\n",
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"\n",
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"Reduce the number of data points by sampling a subset of the data and use transparency to help visualize the density of overlapping points."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Downsample the data by taking every nth point\n",
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"n = 1 # Adjust this value as needed\n",
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"downsampled_df = df.iloc[::n, :]\n",
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"\n",
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"# Plotting the downsampled data points with alpha blending\n",
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"plt.figure(figsize=(8, 6))\n",
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"plt.plot(downsampled_df['Time'], downsampled_df[downsampled_df.columns[-1]], alpha=0.5, color='blue', label='Data Points')\n",
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"plt.title('Scatter Plot of Downsampled Data Points')\n",
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"plt.xlabel('Time')\n",
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"plt.ylabel('Amp')\n",
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"plt.legend()\n",
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"plt.grid(True)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Line Plot with Rolling Avg"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Calculate the rolling average\n",
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"window_size = 50 # Adjust this value as needed\n",
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"rolling_avg = df[df.columns[-1]].rolling(window=window_size).mean()\n",
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"\n",
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"# Plotting the original data points and the rolling average\n",
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"plt.figure(figsize=(8, 6))\n",
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"plt.plot(df['Time'], df[df.columns[-1]], alpha=0.3, color='blue', label='Original Data')\n",
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"plt.plot(df['Time'], rolling_avg, color='red', label='Rolling Average')\n",
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"plt.title('Line Plot with Rolling Average')\n",
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"plt.xlabel('Time')\n",
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"plt.ylabel('Amp')\n",
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"plt.legend()\n",
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"plt.grid(True)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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||
"source": [
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"### Print Time-domain Features (Single CSV Real Data)"
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]
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},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import sys\n",
|
||
"import os\n",
|
||
"# Assuming the src directory is one level up from the notebooks directory\n",
|
||
"sys.path.append('../src/features')\n",
|
||
"from time_domain_features import FeatureExtractor\n",
|
||
"\n",
|
||
"\n",
|
||
"# Extract features\n",
|
||
"extracted = FeatureExtractor(df[df.columns[-1]])\n",
|
||
"\n",
|
||
"# Format with pandas DataFramw\n",
|
||
"features = pd.DataFrame(extracted.features, index=[0])\n",
|
||
"features\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Print Time-domain Features (Multiple CSV Real Data)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import sys\n",
|
||
"import os\n",
|
||
"import re\n",
|
||
"# Assuming the src directory is one level up from the notebooks directory\n",
|
||
"sys.path.append('../src/features')\n",
|
||
"from time_domain_features import ExtractTimeFeatures # use wrapper function instead of class for easy use\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"#### The function"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Define a function to extract numbers from a filename that later used as labels features\n",
|
||
"def extract_numbers(filename):\n",
|
||
" '''\n",
|
||
" Extract numbers from a filename\n",
|
||
"\n",
|
||
" Parameters\n",
|
||
" ----------\n",
|
||
" filename : str\n",
|
||
" The filename to extract numbers from\n",
|
||
"\n",
|
||
" Returns\n",
|
||
" -------\n",
|
||
" list\n",
|
||
" A list of extracted numbers: [damage_number, test_number, sensor_number]\n",
|
||
" '''\n",
|
||
" # Find all occurrences of one or more digits in the filename\n",
|
||
" numbers = re.findall(r'\\d+', filename)\n",
|
||
" # Convert the list of number strings to integers\n",
|
||
" numbers = [int(num) for num in numbers]\n",
|
||
" # Convert to a tuple and return\n",
|
||
" return numbers\n",
|
||
"\n",
|
||
"def build_features(input_dir:str, sensor:int=None, verbose:bool=False, absolute:bool=False):\n",
|
||
" all_features = []\n",
|
||
" for nth_damage in os.listdir(input_dir):\n",
|
||
" nth_damage_path = os.path.join(input_dir, nth_damage)\n",
|
||
" if verbose:\n",
|
||
" print(f'Extracting features from damage folder {nth_damage_path}')\n",
|
||
" if os.path.isdir(nth_damage_path):\n",
|
||
" for nth_test in os.listdir(nth_damage_path):\n",
|
||
" nth_test_path = os.path.join(nth_damage_path, nth_test)\n",
|
||
" # if verbose:\n",
|
||
" # print(f'Extracting features from {nth_test_path}')\n",
|
||
" if sensor is not None:\n",
|
||
" # Check if the file has the specified sensor suffix\n",
|
||
" if not nth_test.endswith(f'_{sensor:02}.csv'):\n",
|
||
" continue\n",
|
||
" # if verbose:\n",
|
||
" # print(f'Extracting features from {nth_test_path}')\n",
|
||
" features = ExtractTimeFeatures(nth_test_path, absolute=absolute) # return the one csv file feature in dictionary {}\n",
|
||
" if verbose:\n",
|
||
" print(features)\n",
|
||
" features['label'] = extract_numbers(nth_test)[0] # add labels to the dictionary\n",
|
||
" features['filename'] = nth_test # add filename to the dictionary\n",
|
||
" all_features.append(features)\n",
|
||
"\n",
|
||
" # Create a DataFrame from the list of dictionaries\n",
|
||
" df = pd.DataFrame(all_features)\n",
|
||
" return df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"#### Execute the automation"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"data_dir = \"D:/thesis/data/converted/raw\"\n",
|
||
"# Extract features\n",
|
||
"df1 = build_features(data_dir, sensor=1, verbose=True, absolute=True)\n",
|
||
"df2 = build_features(data_dir, sensor=2, verbose=True, absolute=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df1.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import seaborn as sns\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# Assuming your DataFrame is named 'df'\n",
|
||
"\n",
|
||
"# Subsetting the DataFrame to include only the first 3 columns and the label\n",
|
||
"subset_df = df1[['Mean', 'Max', 'Peak (Pm)', 'label']]\n",
|
||
"\n",
|
||
"# Plotting the pairplot\n",
|
||
"g = sns.pairplot(subset_df, hue='label', diag_kind='kde')\n",
|
||
"\n",
|
||
"# Adjusting the axis limits\n",
|
||
"# for ax in g.axes.flatten():\n",
|
||
"# ax.set_xlim(-10, 10) # Adjust these limits based on your data\n",
|
||
"# ax.set_ylim(-10, 10) # Adjust these limits based on your data\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df2.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import seaborn as sns\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# Assuming your DataFrame is named 'df'\n",
|
||
"\n",
|
||
"# Subsetting the DataFrame to include only the first 3 columns and the label\n",
|
||
"subset_df = df2[['Mean', 'Max', 'Standard Deviation', 'Kurtosis', 'label']]\n",
|
||
"\n",
|
||
"# Plotting the pairplot\n",
|
||
"g = sns.pairplot(subset_df, hue='label', diag_kind='kde')\n",
|
||
"\n",
|
||
"# Adjusting the axis limits\n",
|
||
"# for ax in g.axes.flatten():\n",
|
||
"# ax.set_xlim(-10, 10) # Adjust these limits based on your data\n",
|
||
"# ax.set_ylim(-10, 10) # Adjust these limits based on your data\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"##### Perform division"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Separate the label column\n",
|
||
"label_column = df1.iloc[:, -2]\n",
|
||
"\n",
|
||
"# Perform the relative value by operate division on all the features\n",
|
||
"df_relative = df2.iloc[:, :-2] / df1.iloc[:, :-2]\n",
|
||
"\n",
|
||
"# Add the label column back to the resulting DataFrame\n",
|
||
"df_relative['label'] = label_column\n",
|
||
"\n",
|
||
"# Append a string to all column names\n",
|
||
"suffix = '_rel'\n",
|
||
"df_relative.columns = [col + suffix if col != 'label' else col for col in df_relative.columns]\n",
|
||
"\n",
|
||
"# Display the first 5 rows of the resulting DataFrame\n",
|
||
"df_relative"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"#### Subsetting DataFrame to see the pair plots due to many features"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import seaborn as sns\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# Assuming your DataFrame is named 'df'\n",
|
||
"\n",
|
||
"# Subsetting the DataFrame to include only the first 3 columns and the label\n",
|
||
"subset_df = df_relative[['Mean_rel', 'Max_rel', 'Peak (Pm)_rel', 'label']]\n",
|
||
"\n",
|
||
"# Plotting the pairplot\n",
|
||
"g = sns.pairplot(subset_df, hue='label', diag_kind='kde')\n",
|
||
"\n",
|
||
"# Adjusting the axis limits\n",
|
||
"# for ax in g.axes.flatten():\n",
|
||
"# ax.set_xlim(-10, 10) # Adjust these limits based on your data\n",
|
||
"# ax.set_ylim(-10, 10) # Adjust these limits based on your data\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
}
|
||
],
|
||
"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": "3.10.8"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|