{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1D array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10,  2,  4, 50])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([10, 2, 4, 50])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50, 10,  2,  4, 50, 10])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.pad(a, pad_width=1, mode=\"wrap\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2D array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10,  2,  4, 50],\n",
       "       [ 5,  4,  1, -3]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[10, 2, 4, 50], [5, 4, 1, -3]])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3,  5,  4,  1, -3,  5],\n",
       "       [50, 10,  2,  4, 50, 10],\n",
       "       [-3,  5,  4,  1, -3,  5],\n",
       "       [50, 10,  2,  4, 50, 10]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.pad(a, pad_width=1, mode=\"wrap\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## vector field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1, 1), (0, 0), (0, 0), (0, 0))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.ones((3, 2, 2, 3))\n",
    "\n",
    "import discretisedfield as df\n",
    "\n",
    "padding_sequence = df.util.assemble_index((0, 0), 4, {0: (1, 1)})\n",
    "padding_sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.]]],\n",
       "\n",
       "\n",
       "       [[[1., 1., 1.],\n",
       "         [1., 1., 1.]],\n",
       "\n",
       "        [[1., 1., 1.],\n",
       "         [1., 1., 1.]]],\n",
       "\n",
       "\n",
       "       [[[1., 1., 1.],\n",
       "         [1., 1., 1.]],\n",
       "\n",
       "        [[1., 1., 1.],\n",
       "         [1., 1., 1.]]],\n",
       "\n",
       "\n",
       "       [[[1., 1., 1.],\n",
       "         [1., 1., 1.]],\n",
       "\n",
       "        [[1., 1., 1.],\n",
       "         [1., 1., 1.]]],\n",
       "\n",
       "\n",
       "       [[[0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.]]]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.pad(a, pad_width=((1, 1), (0, 0), (0, 0), (0, 0)), mode=\"constant\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[1., 1., 1.],\n",
       "         [1., 1., 1.]],\n",
       "\n",
       "        [[1., 1., 1.],\n",
       "         [1., 1., 1.]]],\n",
       "\n",
       "\n",
       "       [[[1., 1., 1.],\n",
       "         [1., 1., 1.]],\n",
       "\n",
       "        [[1., 1., 1.],\n",
       "         [1., 1., 1.]]],\n",
       "\n",
       "\n",
       "       [[[1., 1., 1.],\n",
       "         [1., 1., 1.]],\n",
       "\n",
       "        [[1., 1., 1.],\n",
       "         [1., 1., 1.]]]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-c5e3a0657cac>:1: FutureWarning: in the future negative indices will not be ignored by `numpy.delete`.\n",
      "  np.delete(a, (0, -1), axis=0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[[1., 1., 1.],\n",
       "         [1., 1., 1.]],\n",
       "\n",
       "        [[1., 1., 1.],\n",
       "         [1., 1., 1.]]],\n",
       "\n",
       "\n",
       "       [[[1., 1., 1.],\n",
       "         [1., 1., 1.]],\n",
       "\n",
       "        [[1., 1., 1.],\n",
       "         [1., 1., 1.]]]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.delete(a, (0, -1), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10,  2,  4, 50],\n",
       "       [ 5,  4,  1, -3]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[10, 2, 4, 50], [5, 4, 1, -3]])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 4],\n",
       "       [4, 1]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.delete(a, (0, a.shape[1] - 1), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1.])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 2, 3, 4])\n",
    "np.gradient(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 0])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa = np.pad(a, (1, 1))\n",
    "pa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1. ,  1. ,  1. ,  1. , -1.5, -4. ])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.gradient(pa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[1., 1., 1.]]],\n",
       "\n",
       "\n",
       "       [[[1., 1., 1.]]]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.ones((2, 1, 1, 3))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 1, 1, 3)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.pad(a, ((1, 1), (0, 0), (0, 0), (0, 0))).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}