diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..3dff88ecc71ba0722e068e0affecea3566bd832c
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+.idea/
+.ipynb_checkpoints/
+__pycache__/
diff --git a/hp4155/.ipynb_checkpoints/CTLM_final-checkpoint.ipynb b/hp4155/.ipynb_checkpoints/CTLM_final-checkpoint.ipynb
deleted file mode 100644
index 363fcab7ed6e9634e198cf5555ceb88932c9a245..0000000000000000000000000000000000000000
--- a/hp4155/.ipynb_checkpoints/CTLM_final-checkpoint.ipynb
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/hp4155/.ipynb_checkpoints/I-V_new-checkpoint.ipynb b/hp4155/.ipynb_checkpoints/I-V_new-checkpoint.ipynb
deleted file mode 100644
index de5b44bd6a6e5be7234707072776b8ea21ec1256..0000000000000000000000000000000000000000
--- a/hp4155/.ipynb_checkpoints/I-V_new-checkpoint.ipynb
+++ /dev/null
@@ -1,138 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "8b118521-4ded-405e-95fd-3c5642ef8585",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import measurements\n",
-    "from measurements import *"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "2d031d5e-a683-4def-8bc0-9008e5ae87eb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     Voltage(V)    Current(A)\n",
-      "0          0.00 -1.335500e-08\n",
-      "1          0.05  1.470300e-08\n",
-      "2          0.10 -9.288000e-09\n",
-      "3          0.15  1.404500e-08\n",
-      "4          0.20 -9.236000e-09\n",
-      "..          ...           ...\n",
-      "196        9.80 -1.444000e-08\n",
-      "197        9.85  6.803000e-09\n",
-      "198        9.90 -1.186100e-08\n",
-      "199        9.95  1.108700e-08\n",
-      "200       10.00 -1.354400e-08\n",
-      "\n",
-      "[201 rows x 2 columns]\n"
-     ]
-    },
-    {
-     "ename": "OSError",
-     "evalue": "[Errno 22] Invalid argument: '_results_2023-09-07 13:07:41.csv'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[2], line 5\u001b[0m\n\u001b[0;32m      2\u001b[0m step \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.05\u001b[39m\n\u001b[0;32m      3\u001b[0m stop \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m10\u001b[39m\n\u001b[1;32m----> 5\u001b[0m \u001b[43mI_V_Measurement\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32m~\\labcode\\hp4155\\measurements.py:64\u001b[0m, in \u001b[0;36mI_V_Measurement\u001b[1;34m(start, stop, step)\u001b[0m\n\u001b[0;32m     62\u001b[0m \u001b[38;5;66;03m#exporting the data frame in an excel file\u001b[39;00m\n\u001b[0;32m     63\u001b[0m file_name \u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_results_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdate\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m---> 64\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     66\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m device\n",
-      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\generic.py:3772\u001b[0m, in \u001b[0;36mNDFrame.to_csv\u001b[1;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, decimal, errors, storage_options)\u001b[0m\n\u001b[0;32m   3761\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, ABCDataFrame) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mto_frame()\n\u001b[0;32m   3763\u001b[0m formatter \u001b[38;5;241m=\u001b[39m DataFrameFormatter(\n\u001b[0;32m   3764\u001b[0m     frame\u001b[38;5;241m=\u001b[39mdf,\n\u001b[0;32m   3765\u001b[0m     header\u001b[38;5;241m=\u001b[39mheader,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   3769\u001b[0m     decimal\u001b[38;5;241m=\u001b[39mdecimal,\n\u001b[0;32m   3770\u001b[0m )\n\u001b[1;32m-> 3772\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDataFrameRenderer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mformatter\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_csv\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   3773\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_or_buf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3774\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlineterminator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlineterminator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3775\u001b[0m \u001b[43m    \u001b[49m\u001b[43msep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msep\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3776\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3777\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3778\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3779\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquoting\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquoting\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3780\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3781\u001b[0m \u001b[43m    \u001b[49m\u001b[43mindex_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex_label\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3782\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3783\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunksize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunksize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3784\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquotechar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquotechar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3785\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdate_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdate_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3786\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdoublequote\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdoublequote\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3787\u001b[0m \u001b[43m    \u001b[49m\u001b[43mescapechar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mescapechar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3788\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3789\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\io\\formats\\format.py:1186\u001b[0m, in \u001b[0;36mDataFrameRenderer.to_csv\u001b[1;34m(self, path_or_buf, encoding, sep, columns, index_label, mode, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, errors, storage_options)\u001b[0m\n\u001b[0;32m   1165\u001b[0m     created_buffer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m   1167\u001b[0m csv_formatter \u001b[38;5;241m=\u001b[39m CSVFormatter(\n\u001b[0;32m   1168\u001b[0m     path_or_buf\u001b[38;5;241m=\u001b[39mpath_or_buf,\n\u001b[0;32m   1169\u001b[0m     lineterminator\u001b[38;5;241m=\u001b[39mlineterminator,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1184\u001b[0m     formatter\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfmt,\n\u001b[0;32m   1185\u001b[0m )\n\u001b[1;32m-> 1186\u001b[0m \u001b[43mcsv_formatter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m created_buffer:\n\u001b[0;32m   1189\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(path_or_buf, StringIO)\n",
-      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\io\\formats\\csvs.py:240\u001b[0m, in \u001b[0;36mCSVFormatter.save\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    236\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    237\u001b[0m \u001b[38;5;124;03mCreate the writer & save.\u001b[39;00m\n\u001b[0;32m    238\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    239\u001b[0m \u001b[38;5;66;03m# apply compression and byte/text conversion\u001b[39;00m\n\u001b[1;32m--> 240\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    241\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    242\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    243\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    244\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    245\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    246\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    247\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m handles:\n\u001b[0;32m    248\u001b[0m     \u001b[38;5;66;03m# Note: self.encoding is irrelevant here\u001b[39;00m\n\u001b[0;32m    249\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwriter \u001b[38;5;241m=\u001b[39m csvlib\u001b[38;5;241m.\u001b[39mwriter(\n\u001b[0;32m    250\u001b[0m         handles\u001b[38;5;241m.\u001b[39mhandle,\n\u001b[0;32m    251\u001b[0m         lineterminator\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlineterminator,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    256\u001b[0m         quotechar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mquotechar,\n\u001b[0;32m    257\u001b[0m     )\n\u001b[0;32m    259\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_save()\n",
-      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\io\\common.py:859\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    854\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    855\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m    856\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m    857\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m    858\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 859\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m    860\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    861\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    862\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    863\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    864\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    865\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    866\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    867\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m    868\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
-      "\u001b[1;31mOSError\u001b[0m: [Errno 22] Invalid argument: '_results_2023-09-07 13:07:41.csv'"
-     ]
-    }
-   ],
-   "source": [
-    "start = 0\n",
-    "step = 0.05\n",
-    "stop = 10\n",
-    "\n",
-    "I_V_Measurement(start,stop,step)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "2bcfdbc2-8b0a-4d41-b8bf-fca0da5b1e08",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'resisance_values' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mctlm\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32m~\\labcode\\hp4155\\measurements.py:216\u001b[0m, in \u001b[0;36mctlm\u001b[1;34m(field_name, start, stop, step, comp, distances, time)\u001b[0m\n\u001b[0;32m    212\u001b[0m \u001b[38;5;66;03m#save measurement as txt file\u001b[39;00m\n\u001b[0;32m    213\u001b[0m \u001b[38;5;66;03m#add title to the results\u001b[39;00m\n\u001b[0;32m    214\u001b[0m header \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVoltage(V)\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCurrent(A)\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mResistance(Ohm)\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m--> 216\u001b[0m data \u001b[38;5;241m=\u001b[39m {header[\u001b[38;5;241m0\u001b[39m]:voltage_values,header[\u001b[38;5;241m1\u001b[39m]:current_values,header[\u001b[38;5;241m2\u001b[39m]:\u001b[43mresisance_values\u001b[49m}\n\u001b[0;32m    217\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(data)\n\u001b[0;32m    218\u001b[0m \u001b[38;5;28mprint\u001b[39m(df)\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'resisance_values' is not defined"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ctlm()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3ae7daab-6fed-467d-80e0-986aa2a38ce1",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/hp4155/.ipynb_checkpoints/common_used_functionalities-checkpoint.py b/hp4155/.ipynb_checkpoints/common_used_functionalities-checkpoint.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/hp4155/.ipynb_checkpoints/first_goal_new-checkpoint.ipynb b/hp4155/.ipynb_checkpoints/first_goal_new-checkpoint.ipynb
deleted file mode 100644
index 363fcab7ed6e9634e198cf5555ceb88932c9a245..0000000000000000000000000000000000000000
--- a/hp4155/.ipynb_checkpoints/first_goal_new-checkpoint.ipynb
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/hp4155/.ipynb_checkpoints/measurements-checkpoint.py b/hp4155/.ipynb_checkpoints/measurements-checkpoint.py
deleted file mode 100644
index 8d2afca32f1630ef4ae7224196e021e7645c43e3..0000000000000000000000000000000000000000
--- a/hp4155/.ipynb_checkpoints/measurements-checkpoint.py
+++ /dev/null
@@ -1,459 +0,0 @@
-# this is a python file that minimizes the amount of code seen to the user
-
-import module
-import matplotlib.pyplot as plt
-import pandas as pd
-from datetime import datetime
-import os 
-from sklearn.linear_model import LinearRegression
-import sys
-import numpy as np
-from IPython.display import display, clear_output
-
-
-def I_V_Measurement(start,stop,step):
-    
-    device = module.HP4155a('GPIB0::17::INSTR')
-    device.reset()
-    device.inst.write(":PAGE:MEAS")
-    device.inst.write(":PAGE:CHAN:MODE SWEEP") #go to sweep page and prepare sweep measurement
-   
-    #setup sweep
-    device.inst.write(":PAGE:CHAN:MODE SWEEP") #go to sweep page and prepare sweep measurement
-
-    #smu2 and smu4 are disabled
-    device.smu_disable_sweep(2)
-    device.smu_disable_sweep(4)
-
-    #smu1 is constant and common
-    device.smu_mode_meas(1,'COMM')
-    device.smu_function_sweep(1,'CONS')
-
-    #smu3 is VAR1 and V
-    device.smu_mode_meas(3,'V')
-    device.smu_function_sweep(3,'VAR1')
-
-    #define start-step-stop
-    device.start_value_sweep(start)
-    device.step_sweep(step)
-    device.stop_value_sweep(stop)
-    
-    #start measurement
-    device.single_measurement()
-    while device.operation_completed() == False:
-        pass
-    
-    voltage_values = device.return_data('V3')
-    current_values = device.return_data('I3')
-    
-    # show plot
-    plt.plot(voltage_values,current_values)
-    plt.xlabel('Voltage(V)')
-    plt.ylabel('Current(A)')
-    plt.title("I-V plot")
-    plt.show()
-    
-    #export data to csv file
-    #add title to the results
-    
-    header = ['Voltage(V)', 'Current(A)']
-
-    data = {header[0]:voltage_values,header[1]:current_values}
-    df = pd.DataFrame(data)
-    date = str(datetime.today().replace(microsecond=0))
-    
-    print(df)
-    
-    #exporting the data frame in an excel file
-    file_name ="results.csv"
-    path = r"\\fileserver.cst.rwth-aachen.de\public\Datentransfer\Asonitis, Alexandros"
-    #r"C:\Users\user\Desktop"
-    directory = os.path.join(path,file_name)
-    df.to_csv(directory)
-    
-    del device
-
-def stress_sampling(V2_stress=10,V3_stress=3,stress_time=30,V2_sampling=10,V3_sampling=2,sampling_mode='L10',
-                   number_of_points=21,integration_time='SHOR'):
-    
-    #connect to device
-    device = module.HP4155a('GPIB0::17::INSTR')
-    device.reset()
-    
-    device.stress_page()
-    
-    #define smus to numbers in mode and values for stress 
-
-    #mode
-    device.smu_mode(1,'COMM')
-    device.smu_mode(2,'V')
-    device.smu_mode(3,'V')
-    device.smu_mode(4,'COMM')
-    device.sync(4,0)
-
-    #values
-    device.smu_value(2,V2_stress)
-    device.smu_value(3,V3_stress)
-    #time
-    zeit=device.stress_time(stress_time)
-    
-    device.start_stress()
-    while device.operation_completed() == False:
-        pass
-    
-    #start with sampling measurement
-    device.measurement_mode('SAMP')
-
-    #set the mode of smus for sampling
-    device.smu_mode_meas(1,'COMM')
-    device.smu_mode_meas(2,'V')
-    device.smu_mode_meas(3,'V')
-    device.smu_mode_meas(4,'COMM')
-
-
-    #set the values of smu for sampling
-    device.constant_smu_sampling(2,V2_sampling)
-    device.constant_smu_sampling(3,V3_sampling)
-    
-    
-    #time log10
-    device.sampling_mode(sampling_mode)
-
-    #minimum initial interval
-    device.initial_interval('MIN')
-
-    device.number_of_points(number_of_points)
-    device.integration_time(integration_time)
-    
-    device.single_measurement()
-    while device.operation_completed() == False:
-        pass
-
-    time_values = device.return_data('@TIME')
-    I3_values = device.return_data('I3')
-    I2_values = device.return_data('I2')
-    
-    fig = plt.figure()
-    plt.plot(time_values,I2_values,label='I2')
-    plt.plot(time_values,I3_values,label='I3')
-    plt.xlabel('Time(s)')
-    plt.ylabel('Current(A)')
-    plt.title("stress + sampilng plot")
-    plt.legend()
-    plt.show()
-    
-    del device
-    
-
-#prepare full measurement
-def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),comp=10,distances=(5,10,15,25,45),time='MED',innen=0):
-    
-    #connect to the device
-    device = module.HP4155a('GPIB0::17::INSTR')
-    date = str(datetime.today().replace(microsecond=0))
-    
-    #initilize figure
-    plt.figure()
-    plt.xlabel('Voltage(V)')
-    plt.ylabel('Current(A)')
-    plt.title("CTLM plot")
-    
-    #lists for appending all data values
-    ctlm_voltage = []
-    ctlm_current = []
-    ctlm_resistance = []
-    #execute five measurements
-    for j in range(len(distances)):
-        
-        #setup
-        device.reset()
-        device.inst.write(":PAGE:MEAS")
-        device.inst.write(":PAGE:CHAN:MODE SWEEP") #go to sweep page and prepare sweep measurement
-
-        #vsus and vmus are disabled
-        device.disable_vsu(1)
-        device.disable_vsu(2)
-        device.disable_vmu(1)
-        device.disable_vmu(2)
-        #smu1 is constant and common
-        device.smu_mode_meas(1,'COMM')
-        device.smu_function_sweep(1,'CONS')
-    
-        #smu2 is constant and I
-        device.smu_mode_meas(2,'I')
-        device.smu_function_sweep(2,'CONS')
-        device.cons_smu_value(2,0)
-    
-        #smu3 is var1 and I
-        device.smu_mode_meas(3,'I')
-        device.smu_function_sweep(3,'VAR1')
-    
-        #smu4 is constant and I
-        device.smu_mode_meas(4,'I')
-        device.smu_function_sweep(4,'CONS')
-        device.cons_smu_value(4,0)
-    
-        #select compliance of smu3
-        device.comp('VAR1',comp)
-    
-        #compliance of smu2 and smu4 is 10V
-        device.const_comp(2,10)
-        device.const_comp(4,10)
-    
-        # smu1 is common and compliance is automatically set to maximum
-        
-        #define user functions 
-        device.user_function('I','A','I3')
-        print(device.error())
-        device.user_function('V','V','V4-V2')
-        print(device.error())
-        device.user_function('R','OHM','DIFF(V,I)')
-        print(device.error())
-        
-        #integration time
-        device.integration_time(time)
-    
-        #define start-step-stop
-        device.start_value_sweep(start)
-        device.step_sweep(step)
-        device.stop_value_sweep(stop)
-    
-        #start measurement
-        device.single_measurement()
-        while device.operation_completed() == False:
-            pass
-    
-    
-        voltage_values = device.return_data('V3')
-        current_values = device.return_data('I3')
-        voltage = device.return_data('V') 
-        print(voltage_values)
-        print(current_values)
-        
-        ctlm_voltage.append(voltage_values)
-        ctlm_current.append(current_values)
-        
-        #plot results of the single measurement
-        #plt.plot(voltage_values,current_values,label=f"distance={distances[j]}")
-        plt.plot(voltage_values,current_values,label='ausgangvoltage')
-        plt.legend()
-        plt.show()
-        plt.figure()
-        plt.plot(voltage,current_values,label='Eingangsvoltage')
-        plt.legend()
-        plt.show()
-        
-        #save measurement as txt file
-        #add title to the results
-        header = ['Voltage(V)', 'Current(A)','Resistance(Ohm)']
-
-        data = {header[0]:voltage_values,header[1]:current_values}#,header[2]:resisance_values}
-        df = pd.DataFrame(data)
-        print(df)
-        
-        file_name = field_name+"_CTLM_"+str(j+1)+".txt"
-        path =r"\\fileserver.cst.rwth-aachen.de\public\Datentransfer\Asonitis, Alexandros"
-        directory = os.path.join(path,file_name)
-        #export DataFrame to text file (keep header row and index column)
-        f=open(directory, 'a')
-        f.write('title\n')
-        df_string = df.to_string()
-        f.write(df_string)
-        
-        #plot diagramm
-        #plt.legend()
-        #plt.show()
-
-        #wait for confirmation from user after a measurement is done 
-        while True:      
-            answer = input('please press enter to continue with the next measurement or finish after the last measurement!')
-            if answer == "":
-                break
-
-    #close the connection and plot all the diagramms 
-    del device
-
-#tlm/ctlm final part
-def tlm_final(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),comp=10,distances=(5,10,15,25,45),time='MED',innen=0):
-    
-    #connect to the device
-    device = module.HP4155a('GPIB0::17::INSTR')
-    date = str(datetime.today().replace(microsecond=0))
-    
-    #initialize figure
-    fig, (ax1, ax2) = plt.subplots(2,sharex=True,figsize=(8,6)) #the plots share the same x axis 
-    fig.suptitle('CTLM plot')
-    ax1.set_title('I(V)')
-    ax1.set(xlabel='Voltage(V)',ylabel='Current(A)')
-    ax2.set_title('R(V)')
-    ax2.set(xlabel='Voltage(V)',ylabel='Resistance(Ohm)')          
-            
-    #repeat five times
-    for j in range(len(distances)):
-        #setup
-        device.reset()
-        device.inst.write(":PAGE:MEAS")
-        device.inst.write(":PAGE:CHAN:MODE SWEEP") #go to sweep page and prepare sweep measurement
-
-        #disable vmus and vsus
-        device.disable_vsu(1)
-        device.disable_vsu(2)
-        device.disable_vmu(1)
-        device.disable_vmu(2)
-
-        #smu1 is constant and common
-        device.smu_mode_meas(1,'COMM')
-        device.smu_function_sweep(1,'CONS')
-    
-        #smu2 is constant and I
-        device.smu_mode_meas(2,'I')
-        device.smu_function_sweep(2,'CONS')
-        device.cons_smu_value(2,0)
-    
-        #smu3 is var1 and I
-        device.smu_mode_meas(3,'I')
-        device.smu_function_sweep(3,'VAR1')
-    
-        #smu4 is constant and I
-        device.smu_mode_meas(4,'I')
-        device.smu_function_sweep(4,'CONS')
-        device.cons_smu_value(4,0)
-    
-        #select compliance of smu3
-        device.comp('VAR1',comp)
-    
-        #compliance of smu2 and smu4 is 10V
-        device.const_comp(2,10)
-        device.const_comp(4,10)
-
-        #define user functions
-        device.user_function('I','A','I3')
-        device.user_function('V','V','V4-V2')
-        device.user_function('R','OHM','DIFF(V,I)')
-        device.user_function('VS','V','V3')
-        
-
-        #integration time
-        device.integration_time(time)
-        
-        #define start-step-stop
-        device.start_value_sweep(start)
-        device.step_sweep(step)
-        device.stop_value_sweep(stop)
-
-        #display variables
-        device.display_variable('X','V')
-        device.display_variable('Y1','I')
-        device.display_variable('Y2','R')
-
-        device.display_variable_min_max('X','MIN',-10)
-        device.display_variable_min_max('X','MAX',10)
-        device.display_variable_min_max('Y1','MIN',start)
-        device.display_variable_min_max('Y1','MAX',stop)
-        device.display_variable_min_max('Y2','MIN',0)
-        device.display_variable_min_max('Y2','MAX',200)
-
-        #start measurement
-        device.single_measurement()
-        while device.operation_completed() == False:
-                pass
-            
-        #return data from the device
-        
-        V=device.return_data('V')
-        I=device.return_data('I')
-        R=device.return_data('R')
-        
-        # now we have to remove resistance values that R=inf(nan) that means that the current is zero
-        for i in range(len(R)):
-            if R[i]>10**6:
-                R[i]=float('NAN')
-                
-        # plot the results
-        ax1.plot(V,I,label=f"distance={distances[j]}")
-        ax2.plot(V,R,label=f"distance={distances[j]}")
-        ax1.legend(loc='best')
-        ax2.legend(loc="best")
-        clear_output(wait=True)
-        fig.tight_layout()
-        display(fig)
-        
-        #export data frame to csv(for evaluation) and txt 
-        header = ['Voltage(V)', 'Current(A)','Resistance(Ohm)']
-
-        data = {header[0]:V,header[1]:I,header[2]:R}
-        df = pd.DataFrame(data)
-        print(df)
-        
-        #export to txt 
-        #check tlm or ctlm
-        if(innen==0):
-            #specify path and file_name
-            file_name = field_name+"_TLM_"+str(j+1)+".txt"
-            location =r"\\fileserver.cst.rwth-aachen.de\public\Datentransfer\Asonitis, Alexandros"
-            path= os.path.join(location,file_name)
-        
-            #check if file name exists
-            i=1
-            while os.path.exists(path):
-                file_name = field_name+"_TLM_"+str(j+1)+"_"str(i)+".txt"
-                path= os.path.join(location,file_name)
-                i=i+1
-        else:
-            #specify path and file_name
-            file_name = field_name+"_CTLM_"+str(j+1)+".txt"
-            location =r"\\fileserver.cst.rwth-aachen.de\public\Datentransfer\Asonitis, Alexandros"
-            path= os.path.join(location,file_name)
-        
-            #check if file name exists
-            i=1
-            while os.path.exists(path):
-                file_name = field_name+"_CTLM_"+str(j+1)+"_"str(i)+".txt"
-                path= os.path.join(location,file_name)
-                i=i+1
-            
-        title = "measured field:"+field_name+"\ndistance:"+str(distances[j])+"\nI:"+str(start)+"A to "+str(stop)+"A with step:"+str(step)+"\n"
-        
-        f=open(path, 'a')
-        f.write(title)
-        df_string = df.to_string()
-        f.write(df_string)
-        f.close()
-        
-        #export to csv for evaluataion
-        
-         if(innen==0):
-            #specify path and file_name
-            file_name = field_name+"_TLM_"+str(j+1)+".csv"
-            location =r"\\fileserver.cst.rwth-aachen.de\public\Datentransfer\Asonitis, Alexandros"
-            path= os.path.join(location,file_name)
-        
-            #check if file name exists
-            i=1
-            while os.path.exists(path):
-                file_name = field_name+"_TLM_"+str(j+1)+"_"str(i)+".csv"
-                path= os.path.join(location,file_name)
-                i=i+1
-        else:
-            #specify path and file_name
-            file_name = field_name+"_CTLM_"+str(j+1)+".csv"
-            location =r"\\fileserver.cst.rwth-aachen.de\public\Datentransfer\Asonitis, Alexandros"
-            path= os.path.join(location,file_name)
-        
-            #check if file name exists
-            i=1
-            while os.path.exists(path):
-                file_name = field_name+"_CTLM_"+str(j+1)+"_"str(i)+".csv"
-                path= os.path.join(location,file_name)
-                i=i+1
-        
-        df.to_csv(path)
-        
-        # give user confirmation to do the next measurement
-        
-        while True:
-            answer=input("Press enter to continue or anything else to stop the programm:")
-            if answer=="":
-                break
-            else:
-                sys.exit()
diff --git a/hp4155/.ipynb_checkpoints/module-checkpoint.py b/hp4155/.ipynb_checkpoints/module-checkpoint.py
deleted file mode 100644
index 03057b02e62e8c32b9ad7fd78c322dcaf69b6482..0000000000000000000000000000000000000000
--- a/hp4155/.ipynb_checkpoints/module-checkpoint.py
+++ /dev/null
@@ -1,217 +0,0 @@
-import pyvisa
-
-class HP4155a(object):
-    
-    def __init__(self,adress):
-        self.adress = adress
-        self.rm = pyvisa.ResourceManager()
-        self.inst = self.rm.open_resource(adress)
-        self.inst.timeout = None
-    
-    def idn(self):
-       return self.inst.query("*IDN?")
-    
-    def __del__(self):
-        self.rm.close()
-    
-    def reset(self):
-        self.inst.write("*RST")
-
-    #smu mode
-    def smu_mode(self,smu_number,mode):
-        command = f":PAGE:STR:SMU{smu_number}:MODE {mode}"
-        self.inst.write(command)
-    
-    
-    #smu constant value for stress measurement
-    def smu_value(self,smu_number,value):
-        command =f":PAGE:STR:SET:CONS:SMU{smu_number} {value}"
-        self.inst.write(command)
-    
-    #set the stess time in seconds
-    def stress_time(self,time):
-        command = f":PAGE:STR:SET:DUR {time}"
-        self.inst.write(command)
-        return time
-        
-    #start stress operation
-    def start_stress(self):
-        #inst.write(":PAGE:SCONtrol:STRess[:STARt]")
-        self.inst.write(":PAGE:SCON:STR")
-        #inst.write("*TRG")
-        #self.inst.query('*OPC?')
-        
-    #stop current operation
-    def stop_operation(self):
-        self.inst.write(":PAGE:SCONtrol:STOP")
-    
-    #get data from HP4155a
-    def get_data(self):
-        self.inst.write(":FORM REAL")
-        data = self.inst.query(":HCOPy:ITEM:ALL:DATA?")
-        return data
-
-    def sync(self,smu_number,s):
-        if s == 0:
-            mode = "NSYN"
-        else:
-            mode="SYNC"
-        command = f":PAGE:STR:SMU{smu_number}:FUNC {mode}"
-        self.inst.write(command)
-
-    def single_measurement(self):
-        self.inst.write(":PAGE:SCON:SING")
-    
-    #go to stress page 
-    def stress_page(self):
-        self.inst.write(":PAGE:STR")
-    
-    def error(self):
-        return self.inst.query(":SYST:ERR?")
-
-    def operation_completed(self):
-        text = self.inst.query('*OPC?')
-        text = text.replace('\n','')
-        finished = bool(text)
-        return finished
-    
-    def show_variables(self):
-        return self.inst.query(":DATA:CAT?") 
-
-     #sweep functions
-    def smu_disable_sweep(self,number):
-        command= f":PAGE:CHAN:SMU{number}:DIS"
-        self.inst.write(command)
-    
-   #The following function is for both sampling and sweep
-    def smu_mode_meas(self,number,mode):
-        command=f":PAGE:CHAN:SMU{number}:MODE {mode}"
-        self.inst.write(command)
-    
-    def smu_function_sweep(self,number,function):
-        command=f":PAGE:CHAN:SMU{number}:FUNC {function}"
-        self.inst.write(command)
-
-    def start_value_sweep(self,value):
-        command=f":PAGE:MEAS:VAR1:START {value}"
-        self.inst.write(command)
-    
-    def step_sweep(self,value):
-        command=f":PAGE:MEAS:VAR1:STEP {value}"
-        self.inst.write(command)
-        
-    def stop_value_sweep(self,value):
-        command=f":PAGE:MEAS:VAR1:STOP {value}"
-        self.inst.write(command)
-
-    def cons_smu_value(self,smu_number,value):
-        command =f"PAGE:MEAS:CONS:SMU{smu_number} {value}"
-        self.inst.write(command)
-        
-        ''' smu1 is constant and common, SMU3 is controlled through the Var1(v) which has a start value, a stop value, and a step
-        We don't need SMU2 and SMU4
-        
-        :PAGE:CHANnels[:CDEFinition]:SMU<n>:FUNCtion this command is used to define the variable of SMU3
-        :PAGE:CHANnels[:CDEFinition]:SMU<n>:MODE
-        This command sets the output MODE of SMU<n>. This command also has a query
-        form. It is different that the one we used for the stress setup. 
-        :PAGE:CHANnels[:CDEFinition]:SMU<n>:DISable
-        Important! we dont set a value for smu1.'''
-
-    #sampling measure functions part2 first goal
-
-    ''' some instructions
-    we need linear log10, log25 and thinned out (one function also log50 can be selected) SCPI COMMAND: :PAGE:MEASure:SAMPling:MODE
-    Delay time(Hold time in manual) is to be seleceted :PAGE:MEASure:SAMPling:HTIMe
-    Minimum initial interval :PAGE:MEASure:SAMPling:IINTerval
-    number of points: :PAGE:MEASure:SAMPling:POINts
-    integration time: :PAGE:MEASure:MSETup:ITIMe[:MODE]
-    '''
-    def sampling_mode(self,mode):
-        command = f"PAGE:MEAS:SAMP:MODE {mode}"
-        self.inst.write(command)
-    
-    def delay_time(self,time):
-        command = f":PAGE:MEAS:SAMP:HTIM {time}"
-        self.inst.write(command)
-
-    def initial_interval(self,interval):
-        command = f":PAGE:MEAS:SAMP:IINT {interval}"
-        self.inst.write(command)
-
-    def number_of_points(self,number):
-        command = f":PAGE:MEAS:SAMP:POIN {number}"
-        self.inst.write(command)
-
-    #integration time is SHOR,MED,LONG   
-    def integration_time(self,time):
-        command=f":PAGE:MEAS:MSET:ITIM {time}"
-        self.inst.write(command)
-
-    ''' The smus need to be set to other values as seen in the labview script we can define the following functions
-    the vgs is v3 and vds is v2 (constant values) we need current-time diagramm(id,ig) v3=2V, v2=10V
-     then we have to save the results in a file'''
-
-    def constant_smu_sampling(self,number,value):
-        command =f":PAGE:MEAS:SAMP:CONS:SMU{number} {value}"
-        self.inst.write(command)
-    
-    def measurement_mode(self,mode):
-        command =f":PAGE:CHAN:MODE {mode}"
-        self.inst.write(command)
-        
-    #this is a method that returns data from a variable and converts them to a list of real numbers
-    def return_data(self, variable):
-        
-        #send command to instrument returns a string of comma seperated values
-        command = f":DATA? '{variable}'" 
-        data = self.inst.query(command)
-        
-        # separate the string to a list of strings
-        values = data.replace("\n",",").split(",")
-        values.pop()
-        
-        #convert the string to float numbers 
-        for i in range(len(values)):
-            values[i] = float(values[i])
-        
-        return values
-
-    # these are commands for the compliance
-    
-    #set the power compliance for VAR1 and VAR2
-    def pcomp(self,variable,value):
-        command = f":PAGE:MEAS:{variable}:PCOM {value}"
-        self.inst.write(command)
-        
-    #set normal compliance for VAR1 and VAR2
-    def comp(self,variable,value):
-        """ """
-        command = f":PAGE:MEAS:{variable}:COMP {value}"
-        self.inst.write(command)
-    
-    # constant voltage compiance of an smu(only use if the smu is constant and the mode is not COMMON)
-    
-    def const_comp(self,smu_number,value):
-        command = f":PAGE:MEAS:CONS:SMU{smu_number}:COMP {value}"
-        self.inst.write(command)
-
-    #this command disables the VSU1 and VSU2
-    def disable_vsu(self, vsu_number):
-        command = f":PAGE:CHAN:VSU{vsu_number}:DIS"
-        self.inst.write(command)
-
-    def disable_vmu(self,vmu_number):
-        command = f":PAGE:CHAN:VMU{vmu_number}:DIS"
-        self.inst.write(command)
-        
-    #this command is for defining a new user function
-    def user_function(self,name,unit,expression):
-        command = f":PAGE:CHAN:UFUN:DEF '{name}','{unit}','{expression}'"
-        self.inst.write(command)
-
-    #this command is for displaying the correct variables
-    def display_variable(self,axis,variable):
-        command=f":PAGE:DISP:GRAP:{axis}:NAME '{variable}'"
-        self.inst.write(command)
-        
\ No newline at end of file
diff --git a/hp4155/.ipynb_checkpoints/tests-checkpoint.ipynb b/hp4155/.ipynb_checkpoints/tests-checkpoint.ipynb
deleted file mode 100644
index 363fcab7ed6e9634e198cf5555ceb88932c9a245..0000000000000000000000000000000000000000
--- a/hp4155/.ipynb_checkpoints/tests-checkpoint.ipynb
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/hp4155/.ipynb_checkpoints/tlm_final-checkpoint.ipynb b/hp4155/.ipynb_checkpoints/tlm_final-checkpoint.ipynb
deleted file mode 100644
index 363fcab7ed6e9634e198cf5555ceb88932c9a245..0000000000000000000000000000000000000000
--- a/hp4155/.ipynb_checkpoints/tlm_final-checkpoint.ipynb
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/hp4155/.ipynb_checkpoints/tlm_parameters-checkpoint.ipynb b/hp4155/.ipynb_checkpoints/tlm_parameters-checkpoint.ipynb
deleted file mode 100644
index 363fcab7ed6e9634e198cf5555ceb88932c9a245..0000000000000000000000000000000000000000
--- a/hp4155/.ipynb_checkpoints/tlm_parameters-checkpoint.ipynb
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/hp4155/__pycache__/hp4155a_class.cpython-311.pyc b/hp4155/__pycache__/hp4155a_class.cpython-311.pyc
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diff --git a/hp4155/__pycache__/measurements.cpython-311.pyc b/hp4155/__pycache__/measurements.cpython-311.pyc
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diff --git a/hp4155/__pycache__/measurements.cpython-39.pyc b/hp4155/__pycache__/measurements.cpython-39.pyc
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diff --git a/hp4155/__pycache__/module.cpython-311.pyc b/hp4155/__pycache__/module.cpython-311.pyc
deleted file mode 100644
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diff --git a/hp4155/__pycache__/module.cpython-39.pyc b/hp4155/__pycache__/module.cpython-39.pyc
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diff --git a/keitley244/.ipynb_checkpoints/keitley_224_control_class-checkpoint.py b/keitley244/.ipynb_checkpoints/keitley_224_control_class-checkpoint.py
deleted file mode 100644
index 36916014839508c7b70ecae3c45d7aed669df6a4..0000000000000000000000000000000000000000
--- a/keitley244/.ipynb_checkpoints/keitley_224_control_class-checkpoint.py
+++ /dev/null
@@ -1,182 +0,0 @@
-'''
-KEITHLEY 224 instrument driver
-TODO: SRQ decoding
-https://www.eevblog.com/forum/testgear/keithley-224-python-code/
-'''
-import pyvisa
-import enum
-
-class Readout_Values:
-    def __init__(self):
-        self.raw = ""
-        self.current = 0.0
-        self.overcompliance = False
-        self.voltage = 0.0
-        self.time = 0.0
-
-# Range Commands
-RANGE_LIST = (
-    'R0',
-    'R5',
-    'R6',
-    'R7',
-    'R8',
-    'R9',
-    )
-
-def get_available_devices():
-    rm = pyvisa.ResourceManager()
-    devices = rm.list_resources()
-    rm.close()
-    return devices
-
-def _decode_values(rawdata):
-    splitted = rawdata.split(',')
-    readout = Readout_Values()
-    readout.raw = rawdata
-    for element in splitted:
-        if 'DCI' in element:
-            if element[0] is 'O':
-                readout.overcompliance = True
-            readout.current = float(element[4:])
-        if 'V' in element:
-            readout.voltage = float(element[1:])
-        if 'W' in element:
-            readout.time = float(element[1:])
-    return readout
-
-def _format_e(n):
-    a = '%E' % n
-    return a.split('E')[0].rstrip('0').rstrip('.') + 'E' + a.split('E')[1]
-
-class KEITHLEY_224(object):
-
-    class Ranges(enum.Enum):
-        AUTO = 0
-        MAN_20uA = 1
-        MAN_200uA = 2
-        MAN_2mA = 3
-        MAN_20mA = 4
-        MAN_1m01A = 5
-
-    def __init__(self, address):
-        self._address = address
-        self._rm = pyvisa.ResourceManager()
-        self._inst = self._rm.open_resource(address)
-        self._range = self.Ranges.AUTO
-        self.voltage = 3.0
-        self.current = float(1e-06)
-        self.time = 0.05
-        self.operate = False
-        self._inst.control_ren(1)  ## +++++++++++++++++++++++
-
-    def __del__(self):
-        self.operate = False
-        self._inst.control_ren(0)  ## +++++++++++++++++++++++
-        self._rm.close()
-
-    def get_measurement(self):
-        self._inst.timeout = 1000
-        result = _decode_values(self._inst.read())
-        return result
-
-    @property
-    def range(self):
-        return self._range
-
-    @range.setter
-    def range(self, range):
-        if not isinstance(range, self.Ranges):
-            raise TypeError('mode must be an instance of Ranges Enum')
-        self._range = range
-        self._inst.write(RANGE_LIST[self._range.value]+'X')
-
-    @property
-    def voltage(self):
-        return self._voltage
-
-    @voltage.setter
-    def voltage(self, voltage):
-        if (voltage < 1) or (voltage > 105):
-            raise ValueError('voltage limits: 1 to 105')
-        self._voltage = voltage
-        self._inst.write('V'+ _format_e(voltage)+'X')
-
-    @property
-    def current(self):
-        return self._current
-
-    @current.setter
-    def current(self, current):
-        if (current < -0.101) or (current > 0.101):
-            raise ValueError('current limits: +/- 0.101')
-        self._current = current
-        self._inst.write('I' + _format_e(current) + 'X')
-#        print('I' + _format_e(current) + 'X')  ## +++++++++++++++++++++++
-
-    @property
-    def time(self):
-        return self._time
-
-    @time.setter
-    def time(self, time):
-        if (time < 0.05) or (time > 0.9999):
-            raise ValueError('time limits: 0.05 to 0.9999 sec')
-        self._time = time
-        self._inst.write('W' + _format_e(time) + 'X')
-
-    @property
-    def operate(self):
-        return self._operate
-
-    @operate.setter
-    def operate(self, operate):
-        if type(operate) is not type(True):
-            raise ValueError('operate takes a bool value')
-        self._operate = operate
-        if operate is True:
-            self._inst.write('F1X')
-        else:
-            self._inst.write('F0X')
-
-
-# testing the code
-if __name__ == '__main__':
-    import numpy
-    import time
-
-##    instrument = KEITHLEY_224("GPIB0::15::INSTR")
-##
-##    meas = instrument.get_measurement()
-##    print('Raw data: ' + str(meas.raw))
-##    print('Current: ' + str(meas.current))
-##    print('Overcompliance: ' + str(meas.overcompliance))
-##    print('Voltage: ' + str(meas.voltage))
-##    print('Time: ' + str(meas.time))
-##
-##    instrument.operate = True
-##    instrument.voltage = 15
-##    instrument.time = 0.5
-##
-##    time.sleep(5)
-##    
-##    for i in numpy.arange(0.001,0.015,0.001):
-##        instrument.current = i
-##        time.sleep(5.1)
-##
-##        meas = instrument.get_measurement()
-##        print('Raw data: ' + str(meas.raw))
-##        print('Current: ' + str(meas.current))
-##        print('Overcompliance: ' + str(meas.overcompliance))
-##        print('Voltage: ' + str(meas.voltage))
-##        print('Time: ' + str(meas.time) + '\n\n\n')
-##
-##    del instrument
-
-    instrument = KEITHLEY_224("GPIB0::15::INSTR")
-
-    while True:
-        try:
-            pass
-        except:
-            del instrument