{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 list([1, 299])]\n",
      " [1 list([300, 400])]\n",
      " [2 list([500, 600])]]\n",
      "(3, 2)\n"
     ]
    }
   ],
   "source": [
    "parts = np.array(\n",
    "    [\n",
    "        [0, [1, 299]], \n",
    "        [1, [300, 400]],\n",
    "        [2, [500, 600]],\n",
    "    ],\n",
    "    dtype=object,\n",
    ")\n",
    "print(parts)\n",
    "print(parts.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, list([1, 299])],\n",
       "       [1, list([300, 400])],\n",
       "       [2, list([500, 600])],\n",
       "       [3, list([1, 2])]], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.append(parts, [[3, [1,2]]], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[list([1, 299])],\n",
       "       [list([300, 400])],\n",
       "       [list([500, 600])]], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interval_parts = parts[:,1:]\n",
    "interval_parts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[  1, 299]],\n",
       "\n",
       "        [[300, 400]],\n",
       "\n",
       "        [[500, 600]]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensor = torch.tensor(interval_parts.tolist())\n",
    "tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([  1, 299, 300, 400, 500, 600])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensor.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  1, 299]],\n",
       "\n",
       "       [[300, 400]],\n",
       "\n",
       "       [[500, 600]]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensor.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[list([1, 299]) list([300, 400]) list([500, 600])]\n",
      "(3,)\n"
     ]
    }
   ],
   "source": [
    "new_interval_parts = np.array([], dtype=object)\n",
    "\n",
    "for part in interval_parts:\n",
    "    new_interval_parts = np.append(new_interval_parts, np.array(part, dtype=object))\n",
    "\n",
    "print(new_interval_parts)\n",
    "print(new_interval_parts.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(new_interval_parts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_interval_parts = new_interval_parts.reshape((len(new_interval_parts),1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0]\n",
      " [1]\n",
      " [2]]\n",
      "(3, 1)\n"
     ]
    }
   ],
   "source": [
    "print(parts[:,:1])\n",
    "print(parts[:,:1].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 1)\n",
      "(3, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0, list([1, 299])],\n",
       "       [1, list([300, 400])],\n",
       "       [2, list([500, 600])]], dtype=object)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(parts[:,:1].shape)\n",
    "print(new_interval_parts.shape)\n",
    "\n",
    "np.concatenate([parts[:,:1], new_interval_parts], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4], dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1,2], [3,4]], dtype=object).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, list([3, 4])], dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1, [3,4]], dtype=object).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Hi:\n",
    "    def hi(self, a):\n",
    "        print(a)\n",
    "    def hi(self, a, b):\n",
    "        print(a+b)\n",
    "h = Hi()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "h.hi(1, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3.10.8 64-bit",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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