Add all needed files for notebook.

rt_analyzer/notebooks/beremiz_runtime_measurements/timestamp_notebook.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "439adf9f",
+   "metadata": {},
+   "source": [
+    "## Measurements of Beremiz-IDE / Runtime controlling two couplers with cycle time of 20ms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "a19f317d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b9dad676",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f=open(\"coupler0_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": 13,
+   "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": 25,
+   "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.title(\"Coupler 0 cycle time duration\")\n",
+    "plt.xlabel('Cycles', fontsize=10)\n",
+    "plt.ylabel('ms', fontsize=10)\n",
+    "plt.xticks([])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "1b5ee5ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f=open(\"coupler1_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": 17,
+   "id": "18f16b77",
+   "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": 24,
+   "id": "b44f4d70",
+   "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.title(\"Coupler 1 cycle time duration\")\n",
+    "plt.xlabel('Cycles', fontsize=10)\n",
+    "plt.ylabel('ms', fontsize=10)\n",
+    "plt.xticks([])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "552fc7b3",
+   "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
+}

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": 6,
-   "id": "b9dad676",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "f=open(\"/home/ivan/repos/nexedi/osie/rt_analyzer/channel0_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": 7,
-   "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": 14,
-   "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.title(\"Channel 0\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "1b5ee5ae",
-   "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": 10,
-   "id": "18f16b77",
-   "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": 13,
-   "id": "b44f4d70",
-   "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.title(\"Channel 1\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "552fc7b3",
-   "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
-}