draft.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1067, 76)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.read_csv('../data/JIGSAWS/ts_0.csv')\n",
    "ts.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://github.com/time-series-machine-learning/tsml-java/blob/master/src/main/java/experiments/data/DatasetLists.javam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Shape of X =  (234, 10, 400)\n",
      " Shape of a multivariate signal =  (400, 10)\n",
      " Meta data =  {'problemname': 'handmovementdirection_s1_multivariate_train', 'timestamps': False, 'missing': False, 'univariate': False, 'equallength': True, 'classlabel': True, 'targetlabel': False, 'class_values': ['right', 'forward', 'left', 'backward']}\n"
     ]
    }
   ],
   "source": [
    "from aeon.datasets import load_classification\n",
    "data_set_name = \"HandMovementDirection\"\n",
    "X, y, meta_data = load_classification(data_set_name)\n",
    "print(\" Shape of X = \", X.shape)\n",
    "print(\" Shape of a multivariate signal = \", X[0, :, :].T.shape)\n",
    "print(\" Meta data = \", meta_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc8fa089fd0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(X)):\n",
    "    multivariate_signal = X[i, :, :].T\n",
    "    pd.DataFrame(multivariate_signal).to_csv(f\"../data/UCR/{data_set_name}/ts_{i}.csv\", index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 0\n",
    "multivariate_signal = X[i, :, :].T\n",
    "print(\" Shape of multivariate_signal = \", multivariate_signal.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dsymb-playground",
   "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": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}