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danibene/nk_issue_721_mod.ipynb

Created October 13, 2022 15:44
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nk_issue_721_mod.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyMd4pRbD4r2hFjeZIDbEO9F",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/danibene/e9fecdce9cced55628c53db1d93c2830/nk_issue_721_mod.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "w3u9vxZ9AOUF",
"outputId": "16d5ef99-c0ca-4556-df46-cfb02917bc99"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting git+https://github.com/pjercic/NeuroKit.git@fix_smooth_priors_method\n",
" Cloning https://github.com/pjercic/NeuroKit.git (to revision fix_smooth_priors_method) to /tmp/pip-req-build-vn826kl9\n",
" Running command git clone -q https://github.com/pjercic/NeuroKit.git /tmp/pip-req-build-vn826kl9\n",
" Running command git checkout -b fix_smooth_priors_method --track origin/fix_smooth_priors_method\n",
" Switched to a new branch 'fix_smooth_priors_method'\n",
" Branch 'fix_smooth_priors_method' set up to track remote branch 'fix_smooth_priors_method' from 'origin'.\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from neurokit2==0.1.4.1) (3.2.2)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from neurokit2==0.1.4.1) (1.7.3)\n",
"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from neurokit2==0.1.4.1) (1.0.2)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from neurokit2==0.1.4.1) (1.3.5)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from neurokit2==0.1.4.1) (1.21.6)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->neurokit2==0.1.4.1) (3.0.9)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->neurokit2==0.1.4.1) (1.4.4)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->neurokit2==0.1.4.1) (0.11.0)\n",
"Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->neurokit2==0.1.4.1) (2.8.2)\n",
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->neurokit2==0.1.4.1) (4.1.1)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->neurokit2==0.1.4.1) (1.15.0)\n",
"Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->neurokit2==0.1.4.1) (2022.4)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->neurokit2==0.1.4.1) (3.1.0)\n",
"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->neurokit2==0.1.4.1) (1.2.0)\n",
"Building wheels for collected packages: neurokit2\n",
" Building wheel for neurokit2 (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for neurokit2: filename=neurokit2-0.1.4.1-py2.py3-none-any.whl size=1024120 sha256=e7aa3ea3ab2d68738d4564522f77fb27b09bc8f1a38d87934cf94be366f3bbe9\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-xyrl4yxr/wheels/c4/6d/60/4de63779e236b6badf307bcfb15ff4c3eff1c025438bfb608f\n",
"Successfully built neurokit2\n",
"Installing collected packages: neurokit2\n",
"Successfully installed neurokit2-0.1.4.1\n"
]
}
],
"source": [
"!pip install git+https://github.com/pjercic/NeuroKit.git@fix_smooth_priors_method"
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import neurokit2 as nk"
],
"metadata": {
"id": "hHLvp5FLAW7K"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"ecg = nk.ecg_simulate(duration=120, sampling_rate=1000, heart_rate=110, random_state=42)\n",
"\n",
"_, peaks = nk.ecg_process(ecg, sampling_rate=1000)\n",
"peaks = peaks[\"ECG_R_Peaks\"]\n",
"rri = np.diff(peaks).astype(float)\n",
"rri_time = peaks[1:]/1000"
],
"metadata": {
"id": "g6sw2hd9Ao0k"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"detrended_rri = nk.signal_detrend(rri, method=\"tarvainen2002\")"
],
"metadata": {
"id": "1Jekcsi_AqZR"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"plt.figure()\n",
"plt.plot(rri_time, rri, label=\"original\")\n",
"plt.plot(rri_time, detrended_rri, label=\"detrended\")\n",
"plt.legend()\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 265
},
"id": "8AKpLdIuAr4E",
"outputId": "3a6c3649-edf8-4dcb-d180-cbe6ad42abdb"
},
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "PQ_4zc5OAtiR"
},
"execution_count": null,
"outputs": []
}
]
}
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