save data

hp4155/savedata.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d119393c-bc09-4646-a812-d17081d0d105",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hp4155a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "af3fb977-e43b-4120-9e76-8924c8e24d7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = hp4155a.HP4155a('GPIB0::17::INSTR')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ed0d34d0-da93-4f68-8c71-159d47b32347",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time =  device.return_values(\"@TIME\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "fa4ce64d-fb66-407b-bcfb-fe1faeca2df9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "R = device.return_values(\"R\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "dbf0d611-d5a6-49f2-9fc4-e1df92c2a5c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = {\"time(s)\":time,\"R(Ohm)\":R}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "25ba9dff-dfa6-4a2d-8a68-d39e36bd9651",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "977fab8d-7664-4f7d-817c-45f53b2544cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "81f38ff3-0d53-4730-ae4c-d187e23a5f58",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     time(s)        R(Ohm)\n",
+      "0        0.0  2.514458e+07\n",
+      "1       60.0  3.031222e+07\n",
+      "2      120.0  3.103662e+07\n",
+      "3      180.0  3.669725e+07\n",
+      "4      240.0  3.880481e+07\n",
+      "..       ...           ...\n",
+      "846  50760.0 -5.347594e+08\n",
+      "847  50820.0  2.450980e+08\n",
+      "848  50880.0 -2.325581e+09\n",
+      "849  50940.0 -5.154639e+08\n",
+      "850  51000.0 -9.259259e+08\n",
+      "\n",
+      "[851 rows x 2 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "af5de74d-307a-4530-8b63-3fc7f974c7dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('Retention data Bor-Han.txt',\"w\") as f:\n",
+    "    f.write(df.to_string())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "edfa472e-39d6-41cd-b771-a70904207528",
+   "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
+}