diff --git a/Snip Investigation.ipynb b/Snip Investigation.ipynb
index 2e5163c..238c16e 100644
--- a/Snip Investigation.ipynb	
+++ b/Snip Investigation.ipynb	
@@ -10,11 +10,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": 3
+   "version": "3.7.4-final"
   },
   "orig_nbformat": 2,
   "kernelspec": {
-   "name": "python_defaultSpec_1596697988384",
+   "name": "python_defaultSpec_1599316647819",
    "display_name": "Python 3.7.4 64-bit ('tvb': venv)"
   }
  },
@@ -23,7 +23,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 7,
    "metadata": {
     "tags": []
    },
@@ -62,19 +62,19 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "do-not-track\\LCycle_G[0.75]_MouseCortex_Tseries_20200805-031253_.csv\n"
+     "text": "do-not-track\\Old\\5_8_20\\LCycle_G[0.75]_MouseCortex_Tseries_20200805-031253_.csv\n"
     }
    ],
    "source": [
-    "TseriesFile = glob.glob(\"do-not-track/LCycle_G[0.75*Tseries*_.csv\")[1]\n",
-    "ScorrFile = glob.glob(\"do-not-track/LCycle_G[0.75*SCorr*_.csv\")[1]\n",
-    "FCMFile = glob.glob(\"do-not-track/LCycle_G[0.75*FCM*_.csv\")[1]\n",
+    "TseriesFile = glob.glob(\"do-not-track\\\\Old\\\\5_8_20\\\\LCycle_G[0.75*Tseries*_.csv\")[1]\n",
+    "ScorrFile = glob.glob(\"do-not-track\\\\Old\\\\5_8_20\\\\LCycle_G[0.75*SCorr*_.csv\")[1]\n",
+    "FCMFile = glob.glob(\"do-not-track\\\\Old\\\\5_8_20\\\\LCycle_G[0.75*FCM*_.csv\")[1]\n",
     "print(TseriesFile)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 8,
    "metadata": {
     "tags": []
    },
@@ -181,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 19,
    "metadata": {
     "tags": []
    },
@@ -195,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 10,
    "metadata": {
     "tags": []
    },
@@ -203,7 +203,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "1\nSCFC =  SpearmanrResult(correlation=0.39001655130012575, pvalue=1.2354801358988211e-49)\nFCFC =  SpearmanrResult(correlation=0.6129895629303821, pvalue=5.7513349554580164e-70)\n10\nSCFC =  SpearmanrResult(correlation=0.3900971456589239, pvalue=1.175791982143924e-49)\nFCFC =  SpearmanrResult(correlation=0.613030062899865, pvalue=5.601068427039144e-70)\n100\nSCFC =  SpearmanrResult(correlation=0.3900678437055767, pvalue=1.1971537493180992e-49)\nFCFC =  SpearmanrResult(correlation=0.6132501520018289, pvalue=4.850195735306111e-70)\n1000\nSCFC =  SpearmanrResult(correlation=0.39038286360334234, pvalue=9.863772978751427e-50)\nFCFC =  SpearmanrResult(correlation=0.6144452057653352, pvalue=2.215477685245727e-70)\n10000\nSCFC =  SpearmanrResult(correlation=0.3864635172803344, pvalue=1.0813446646225768e-48)\nFCFC =  SpearmanrResult(correlation=0.6161577817065863, pvalue=7.165704114663534e-71)\n100000\nSCFC =  SpearmanrResult(correlation=0.35912096383650566, pvalue=8.039788142483966e-42)\nFCFC =  SpearmanrResult(correlation=0.5921138800646416, pvalue=2.9608885744609305e-64)\n"
+     "text": "1\nSCFC =  SpearmanrResult(correlation=0.39001655130012575, pvalue=1.2354801358988211e-49)\nFCFC =  SpearmanrResult(correlation=0.6129895629303821, pvalue=5.7513349554580164e-70)\n10\nSCFC =  SpearmanrResult(correlation=0.3900971456589239, pvalue=1.175791982143924e-49)\nFCFC =  SpearmanrResult(correlation=0.613030062899865, pvalue=5.601068427039144e-70)\n100\nSCFC =  SpearmanrResult(correlation=0.3900678437055767, pvalue=1.1971537493180992e-49)\nFCFC =  SpearmanrResult(correlation=0.6132501520018289, pvalue=4.850195735306111e-70)\n1000\nSCFC = SpearmanrResult(correlation=0.39038286360334234, pvalue=9.863772978751427e-50)\nFCFC =  SpearmanrResult(correlation=0.6144452057653352, pvalue=2.215477685245727e-70)\n10000\nSCFC =  SpearmanrResult(correlation=0.3864635172803344, pvalue=1.0813446646225768e-48)\nFCFC =  SpearmanrResult(correlation=0.6161577817065863, pvalue=7.165704114663534e-71)\n100000\nSCFC =  SpearmanrResult(correlation=0.35912096383650566, pvalue=8.039788142483966e-42)\nFCFC =  SpearmanrResult(correlation=0.5921138800646416, pvalue=2.9608885744609305e-64)\n"
     }
    ],
    "source": [
@@ -275,13 +275,6 @@
     "Until we snip 1e5 of data (which is like 5/6 of the data) and the usual, since we are taking the correlation of a shorter data length, our error bars indicate that the result we get is worse. \n",
     "This salso uggests, that the snip we have so far already allows the system to reach equilibrium if it ever does. "
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ]
 }
\ No newline at end of file