Initial visualisation / notebook.

rt_analyzer/notebooks/timestamp_notebook.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a19f317d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b9dad676",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f=open(\"/home/ivan/repos/nexedi/osie/rt_analyzer/channel1_duration.txt\", \"r\")\n",
+    "lines = f.readlines()\n",
+    "f.close()\n",
+    "lines = [float(x.replace(\"\\n\", \"\")) for x  in lines[:]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d0d49bb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = {}\n",
+    "i = 0\n",
+    "for x in lines:\n",
+    "    d[i] = x\n",
+    "    i += 1\n",
+    "s = pd.Series(d, name='duration')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "16a57b80",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "s.plot.bar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b5ee5ae",
+   "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": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}