add notebooks

DESU_corr_param_nonparam.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "910b76de-7f01-4548-be82-48845592ea79",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import scipy.stats as stt\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import seaborn as sb\n",
+    "\n",
+    "sb.set_style('whitegrid')\n",
+    "sb.set(font_scale=1.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90388ab5-77ae-4af4-90b4-79251f37a0d0",
+   "metadata": {},
+   "source": [
+    "We generate 2 time series with a linear relationship between them (via a common input).\n",
+    "\n",
+    "### EXERCICE\n",
+    "- Calculate the Pearson correlatrion between the time series (cd `stt.prearsonr`), which also provides you with a p-value for significance.\n",
+    "- Calculate surrogate time series by manipulating the temporal distribution of the time series.\n",
+    "- Make a non-parametric test by comparing the distribution of surrogate correlation values with the value obtained from the original time series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "f1e1d19c-066c-4d71-963c-b702e2363f96",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# duration of time series\n",
+    "T = 100\n",
+    "\n",
+    "# generate two sets of normally distributed random variables (white noise)\n",
+    "x1 = stt.norm.rvs(size=T)\n",
+    "x2 = stt.norm.rvs(size=T)\n",
+    "\n",
+    "# add to create correlation between the two time series\n",
+    "x0 = stt.norm.rvs(size=T)\n",
+    "# desired level of correlation\n",
+    "c = 0.1\n",
+    "x1 = np.sqrt(1.0-c) * x1 + np.sqrt(c) * x0\n",
+    "x2 = np.sqrt(1.0-c) * x2 + np.sqrt(c) * x0"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}