offline inference

notebooks/offline_inference.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# library imports:\n",
+    "\n",
+    "import pandas as pd\n",
+    "from pathlib import Path\n",
+    "import portiloop_software\n",
+    "import torch\n",
+    "from portiloop_software import run_offline_unlabelled, get_final_model_config_dict\n",
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# path to the portiloop software package:\n",
+    "\n",
+    "path_software = Path(portiloop_software.__file__).parent.absolute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# path to the folder containing pre-trained models:\n",
+    "\n",
+    "path_experiments = path_software / 'experiments'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# path to the non-annotated data, change this to your own for custom applications:\n",
+    "\n",
+    "path_data = path_software / 'dataset' / 'example_data_not_annotated.txt'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load non-annotated data into a pytorch Tensor:\n",
+    "\n",
+    "data = torch.Tensor(pd.read_csv(path_data, header=None).values).squeeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f\"The data has been loaded as a torch Tensor of {len(data)} data points:\\n{data}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# configuration dictionary of the model:\n",
+    "\n",
+    "config_dict = get_final_model_config_dict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run offline inference (on all data points):\n",
+    "\n",
+    "output, true_idx = run_offline_unlabelled(config_dict=config_dict,\n",
+    "                                          path_experiments=path_experiments,\n",
+    "                                          unlabelled_segment=data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert result for visualization:\n",
+    "\n",
+    "out = output.numpy() - 5\n",
+    "idx = true_idx.numpy()\n",
+    "sig = data[idx].numpy()\n",
+    "idx_s = idx / config_dict['fe']\n",
+    "last_tick = idx_s[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot results\n",
+    "\n",
+    "start = 115\n",
+    "duration = 10\n",
+    "THRESHOLD = 0.5\n",
+    "\n",
+    "stop = start + duration\n",
+    "\n",
+    "def y1axtoy2ax(y):\n",
+    "    res = y - 5\n",
+    "    return res\n",
+    "\n",
+    "def y1axtoy2ax(y):\n",
+    "    res = y + 5\n",
+    "    return res\n",
+    "\n",
+    "plt.rcParams['figure.figsize'] = [15, 4]\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(idx_s, sig, label=\"Signal\")\n",
+    "ax.plot(idx_s, out, label=\"ANN output\")\n",
+    "ax.hlines(-5 + THRESHOLD, 0, last_tick, colors='lightgrey', label=\"Threshold\", linestyles=':')\n",
+    "ax.set_ylim((-5,5))\n",
+    "ax.set_xlim((start, stop))\n",
+    "ax.set_xlabel(\"Time (s)\")\n",
+    "ax.set_ylabel(\"Signal (arb. unit)\")\n",
+    "secy = ax.secondary_yaxis('right', functions=(y1axtoy2ax, y1axtoy2ax))\n",
+    "secy.set_yticks([0,1])\n",
+    "ax.axes.yaxis.set_visible(False)\n",
+    "ax.set_title(f\"Threshold {THRESHOLD}\")\n",
+    "plt.tight_layout()\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}