{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome and please:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic tricks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Given array $a_0$ that contains integers and indicator array $X$. Return:\n",
    "\n",
    "a) boolean array $b_0 = \\left[\\left\\{\\begin{array}{ll} \\text{True,} & x \\in A\\\\ \\text{False,} & x \\notin A \\end{array}\\right. \\quad\\text{for $x$ in $X$} \\right]$\n",
    "\n",
    "b) list of boolean arrays $b_0, b_1, ..., b_n$ if there are multiple arrays $a_0, a_1, ..., a_n$ given.\n",
    "\n",
    "Source: [my question](https://stackoverflow.com/questions/59656759/what-is-a-best-way-to-intersect-multiple-arrays-with-numpy-array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False  True  True]\n",
      "[0 0 2]\n",
      "[0 1 2 3 4 5]\n",
      "[2 5 4 0]\n",
      "[array([False,  True,  True]), array([ True,  True,  True,  True,  True,  True]), array([ True,  True,  True, False])]\n"
     ]
    }
   ],
   "source": [
    "X = np.array([2,5,0,4,3,1])\n",
    "A = [np.array([-2,0,2]), np.array([0,1,2,3,4,5]), np.array([2,5,4,6])]\n",
    "\n",
    "print(np.isin(A[0], X))\n",
    "\n",
    "Xs, B = np.sort(X), []\n",
    "for a in A:\n",
    "    idx = np.searchsorted(Xs, a) #locations where to append items of a in X\n",
    "    idx[idx==len(Xs)] = 0\n",
    "    print(idx)\n",
    "    B.append(Xs[idx]==a)\n",
    "print(B)\n",
    "\n",
    "#question: what about np.searchsorted(Xs, a, sorter=np.argsort(X))?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Given array of people descriptions. Replace it by labels 0, 1, ... , each of them corresponds to alphabetical order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 0 1 2 2 1 3 2 2 2 1 1 1 2 2 3 3 1]\n",
      "[1 1 1 0 1 2 2 1 3 2 2 2 1 1 1 2 2 3 3 1]\n"
     ]
    }
   ],
   "source": [
    "X = np.array(['Female', 'Female', 'Female', 'Baby', 'Female', 'Male', 'Male', 'Female', 'Senior', 'Male', 'Male', 'Male', 'Female', 'Female', 'Female', 'Male', 'Male',\n",
    " 'Senior', 'Senior', 'Female'])\n",
    "u, rinv = np.unique(X, return_inverse=True)\n",
    "print(rinv)\n",
    "\n",
    "#Alternative way:\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "print(le.fit_transform(rinv)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. An array $X_{x_i}$ of length $n$ is called indicator of term $x_i$ if it's $i$-th item is 1 an any other item is 0. Given an array that contains $n$ distinct countries $c_1$, $c_2$, $c_3$, ..., $c_n$ that might repeat. Replace each of them by indicator array of length $n$. Hint: [numpy broadcasting](https://numpy.org/doc/stable/user/basics.broadcasting.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 0 0]\n",
      " [0 0 1]\n",
      " [1 0 0]\n",
      " [1 0 0]\n",
      " [0 0 1]\n",
      " [0 0 1]\n",
      " [1 0 0]\n",
      " [0 1 0]\n",
      " [1 0 0]\n",
      " [1 0 0]\n",
      " [1 0 0]\n",
      " [0 0 1]\n",
      " [1 0 0]\n",
      " [1 0 0]\n",
      " [0 0 1]\n",
      " [0 1 0]\n",
      " [0 1 0]\n",
      " [0 0 1]\n",
      " [0 0 1]\n",
      " [1 0 0]]\n"
     ]
    }
   ],
   "source": [
    "countries = np.array(['France', 'Spain', 'France', 'France', 'Spain', 'Spain', 'France',\n",
    "       'Germany', 'France', 'France', 'France', 'Spain', 'France',\n",
    "       'France', 'Spain', 'Germany', 'Germany', 'Spain', 'Spain',\n",
    "       'France'])\n",
    "countries_indicator = (countries[:,None] == np.unique(countries)).astype(int) # creating indicator array\n",
    "print(countries_indicator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Given two equal sized 1D arrays $X$ and $Y$ with nonnegative items smaller than $m$ and 2D array $Z$ of size $m \\times m$. Return array that contains $Z_{X_i, Y_i}$ for each $i\\le m$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  6, 18, 23, 24])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z = np.arange(25).reshape((5,5))\n",
    "X, Y = np.array([0, 1, 3, 4, 4]), np.array([0, 1, 3, 3, 4])\n",
    "Z[X, Y]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Given two equal sized 1D arrays $X$ and $Y$ with nonnegative items smaller than $m$ and 2D array $Z$ of size $m \\times m$. Return sub-array of $Z$ that contains rows with indexes taken from $X$ and columns that contains indexes taken from $Y$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  3,  3,  4],\n",
       "       [ 5,  6,  8,  8,  9],\n",
       "       [15, 16, 18, 18, 19],\n",
       "       [20, 21, 23, 23, 24],\n",
       "       [20, 21, 23, 23, 24]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z = np.arange(25).reshape((5,5))\n",
    "X, Y = np.array([0, 1, 3, 4, 4]), np.array([0, 1, 3, 3, 4])\n",
    "Z[np.ix_(X, Y)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Vectorise Python function `product(*arrays)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(4, 2, 0),\n",
       " (4, 2, 1),\n",
       " (4, 2, 2),\n",
       " (4, 4, 0),\n",
       " (4, 4, 1),\n",
       " (4, 4, 2),\n",
       " (4, 0, 0),\n",
       " (4, 0, 1),\n",
       " (4, 0, 2),\n",
       " (3, 2, 0),\n",
       " (3, 2, 1),\n",
       " (3, 2, 2),\n",
       " (3, 4, 0),\n",
       " (3, 4, 1),\n",
       " (3, 4, 2),\n",
       " (3, 0, 0),\n",
       " (3, 0, 1),\n",
       " (3, 0, 2),\n",
       " (1, 2, 0),\n",
       " (1, 2, 1),\n",
       " (1, 2, 2),\n",
       " (1, 4, 0),\n",
       " (1, 4, 1),\n",
       " (1, 4, 2),\n",
       " (1, 0, 0),\n",
       " (1, 0, 1),\n",
       " (1, 0, 2)]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def product1(arrays):\n",
    "    return np.stack(np.meshgrid(*arrays), axis=-1).reshape(-1, len(arrays))\n",
    "def product2(X):\n",
    "    items = [np.array(item) for item in X]\n",
    "    idx = np.where(np.eye(len(X)), Ellipsis, None)\n",
    "    out = [x[tuple(i)] for x,i in zip(items, idx)]\n",
    "    return list(np.broadcast(*out))\n",
    "\n",
    "product2([[4,3,1],[2,4,0],[0,1,2]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Convert iterable `combinations(range(n), k)` to `numpy` array with fastest possible performance. [Source](https://stackoverflow.com/a/63694661/3044825)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [0, 1, 3],\n",
       "       [0, 1, 4],\n",
       "       [0, 2, 3],\n",
       "       [0, 2, 4],\n",
       "       [0, 3, 4],\n",
       "       [1, 2, 3],\n",
       "       [1, 2, 4],\n",
       "       [1, 3, 4],\n",
       "       [2, 3, 4]], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from itertools import combinations\n",
    "\n",
    "def fromiter(iterable, k):\n",
    "    dt = np.dtype([('', np.intp)]*k) #or np.dtype(','.join('i'*5))\n",
    "    indices = np.fromiter(iterable, dt)\n",
    "    indices = indices.view(np.intp).reshape(-1, k)\n",
    "    return indices\n",
    "\n",
    "fromiter(combinations(range(5), 3), 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. Given boolean array, return indices where subarray `[True, False, True]` appears."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rolling(a, window):\n",
    "    shape = (a.size - window + 1, window)\n",
    "    strides = (a.itemsize, a.itemsize)\n",
    "    return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)\n",
    "\n",
    "compare_with = [True, False, True]\n",
    "bool_arr = np.random.choice([True, False], size=20)\n",
    "rolls = rolling(bool_arr, len(compare_with))\n",
    "idx = np.where(np.all(rolls == compare_with, axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9. Solve Diophantine equation `3x+7y+10z=60`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  0  6]\n",
      " [10  0  3]\n",
      " [20  0  0]\n",
      " [ 1  1  5]\n",
      " [11  1  2]\n",
      " [ 2  2  4]\n",
      " [12  2  1]\n",
      " [ 3  3  3]\n",
      " [13  3  0]\n",
      " [ 4  4  2]\n",
      " [ 5  5  1]\n",
      " [ 6  6  0]]\n"
     ]
    }
   ],
   "source": [
    "l = np.array([3,7,10])\n",
    "s = 60\n",
    "unknowns = [range(0, s+1, n) for n in l]\n",
    "triplets = product1(unknowns) #defined as an exerscise before\n",
    "suitable_triplets = triplets[np.sum(triplets, axis=1) == s]\n",
    "solutions = suitable_triplets//l\n",
    "print(solutions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10. Let $L_i(A)$ define the first item of array `A` which doesn't exceed `i`. A sequence $L_{0}(Y), L_{1}(Y), ..., L_{len(X)-1}(Y)$ is called [`left_argmins`](https://stackoverflow.com/questions/63476467/find-argmin-to-the-left-right-of-each-index-in-array) of `X` if `Y` is a sequence of indices that would sort an array `X`. Find `leftt_argmins` of `X`.  What about `right_argmins` of `X`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([7, 5, 4, 1, 9, 2, 3, 6])\n",
    "argidx = np.argsort(x)\n",
    "u, idx = np.unique(argidx, return_index=True)\n",
    "left_argmins = argidx[np.minimum.accumulate(idx)]\n",
    "right_argmins = argidx[np.minimum.accumulate(idx[::-1])][::-1]\n",
    "print(left_argmins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "11. * Given an array `X` and replacement array `Y` of shape `(n, 2)`. Replace all the items of `X` that have corresponding mappings available. [Source](https://stackoverflow.com/questions/53155749/replace-elements-in-numpy-array-avoiding-loops)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 200, 5, 400, 400]\n"
     ]
    }
   ],
   "source": [
    "X = np.array([0,1,2,5,4,4])\n",
    "Y = np.array([[0,1],[1,2],[3,300],[4,400],[2,200]])\n",
    "\n",
    "def replacer(X, Y):\n",
    "    #not vectorized yet\n",
    "    print([Y[Y[:,0]==i, 1][0] if i in Y[:,0] else i for i in X])\n",
    "replacer(X, Y)\n",
    "        \n",
    "# def replacer(X, Y):\n",
    "# sidx = Y[:,0].argsort()\n",
    "# print('sidx', sidx)\n",
    "# sorted_indx = np.searchsorted(Y[:,0], X, sorter=sidx) #where to add items from X in sorted Y to keep ascending order\n",
    "# print('sorted_indx', sorted_indx)\n",
    "# sorted_indx[sorted_indx==len(sidx)] = -1\n",
    "# print('sorted_indx', sorted_indx)\n",
    "# idx_out = sidx[sorted_indx]\n",
    "# print('idx_out', idx_out)\n",
    "# out = Y[idx_out,1]\n",
    "# print('out', out)\n",
    "# out[Y[idx_out, 0]!=X] = 0\n",
    "# print('out', out)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Extract groups of array by first column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([219, 275, 441, 455]), array([468, 494, 533]), array([559, 593, 605]), array([613, 679, 686, 692])]\n",
      "[ 4  7 10 14]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1, 275], [1, 441], [1, 494], [1, 593], [2, 679], [2, 533], [2, 686], [3, 559], [3, 219], [3, 455], [4, 605], [4, 468], [4, 692], [4, 613]])\n",
    "\n",
    "#Way1\n",
    "a.sort(axis=0)\n",
    "u, idx = np.unique(a[:,0], return_index = True) #idx = [0, 4, 7, 10]\n",
    "print(np.split(a[:,1], idx)[1:])\n",
    "\n",
    "#Way2\n",
    "a.sort(axis=0)\n",
    "u, cnt = np.unique(a[:, 0], return_counts=True) #cnt = [4, 3, 3, 4]\n",
    "idx = np.cumsum(cnt)[:-1]\n",
    "print(np.split(a[:,1], idx)[1:])\n",
    "\n",
    "#Way3\n",
    "import numpy_indexed as npi\n",
    "print(npi.group_by(a[:, 0]).split(a[:, 1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Group array by first column and leave only the rows where second column attains maximum. [Source](https://stackoverflow.com/a/63758412/3044825)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 2]\n",
      " [1 4]\n",
      " [2 6]]\n",
      "[[0 2]\n",
      " [1 4]\n",
      " [2 6]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([[2,6],[1,4],[0,1],[1,1],[2,3],[0,2]])\n",
    "a = a[np.argsort(a[:,0])] #sorting lst by first row\n",
    "u, idx = np.unique(a[:,0], return_index = True) \n",
    "print(np.c_[u, np.maximum.reduceat(a[:,1], idx)])\n",
    "\n",
    "import numpy_indexed as npi\n",
    "print(np.transpose(npi.group_by(a[:, 0]).max(a[:, 1])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Group array by first column and sum groups by second column (not using reduceat). [Source](https://stackoverflow.com/a/63750626/3044825)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.2, 40. ],\n",
       "       [ 2.3, 27. ]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def group_by_sum(x):\n",
    "    u, idx = np.unique(x[:,0], return_inverse=True)\n",
    "    s = np.bincount(idx, weights = x[:,1])\n",
    "    return np.c_[u, s]\n",
    "\n",
    "x = np.array([[1.2, 10], [2.3, 20], [1.2, 30], [2.3, 7]])\n",
    "#group_by_sum(x)\n",
    "np.transpose(npi.group_by(x[:, 0]).sum(x[:, 1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "11. Let `X` and `Y` be two polynomials. Each of the polynomials consists of two rows: powers and coeffs, no zero coeffs. Return a new polynomial `X` $\\times$ `Y`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 2 3]\n",
      " [1 2 2 1]]\n"
     ]
    }
   ],
   "source": [
    "import numpy_indexed as npi\n",
    "X = np.array([[0,1],[1,1]])\n",
    "Y = np.array([[0,1,2],[1,1,1]])\n",
    "cells = product1([X[0], Y[0]]) #finding pairs of powers\n",
    "degs = np.sum(cells, axis=1) #summing each pair to get new power\n",
    "coeffs = X[1][cells[:, 0]] * Y[1][cells[:, 1]] #multiplying corresponding coeffs\n",
    "terms = np.column_stack([coeffs, degs]) #result as terms\n",
    "print(np.array(npi.group_by(terms[:, 1]).sum(terms[:,0]))) #reduce similar terms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "15. Given a bunch of groups of ids, return:\n",
    " - list of connected groups (numpy arrays)\n",
    " - ids that tells which groups are connected\n",
    " \n",
    " Source: [my ccarray compilation](..\\ccarray.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([1, 2, 3, 4, 5])], dict_values([array([0, 1, 2, 3], dtype=int64)]))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import igraph as ig\n",
    "\n",
    "def connect_arrays(arrays, return_groups=True):\n",
    "    conc = np.concatenate(arrays)\n",
    "    u, indices = np.unique(conc, return_inverse=True)\n",
    "    bars = [len(n) for n in arrays[:-1]]\n",
    "    bars[0] -= 1\n",
    "    mask = np.ones(len(indices) - 1, dtype=bool)\n",
    "    mask[np.cumsum(bars)] = False\n",
    "    nodes = np.arange(len(u))\n",
    "    edges = (np.array([indices[1:], indices[:-1]]).T)[mask]\n",
    "    graph = ig.Graph()\n",
    "    graph.add_vertices(nodes)\n",
    "    graph.add_edges(edges)\n",
    "    graph_tags = graph.clusters().membership\n",
    "    values = pd.DataFrame(graph_tags).groupby([0]).indices.values()\n",
    "    \n",
    "    if return_groups:\n",
    "        graph_tags = np.array(graph_tags)\n",
    "        pid = [n[0] if len(n) else u[-1] + 1 for n in\n",
    "               arrays]  # if items is zero, add non-existent item larger than any\n",
    "        group_ix = np.searchsorted(u, pid)\n",
    "        bad_indexes = group_ix == len(u)\n",
    "        group_ix[bad_indexes] = 0  # stuff I forced to do to avoid index error\n",
    "        group_tags = graph_tags[group_ix]\n",
    "        group_tags = group_tags.astype(float)\n",
    "        group_tags[bad_indexes] = np.nan\n",
    "        groups = pd.DataFrame(group_tags).groupby([0]).indices.values()\n",
    "        return [u[k] for k in values], groups\n",
    "    else:\n",
    "        return [u[k] for k in values]\n",
    "\n",
    "connect_arrays([[1,2],[2,3],[3,4],[4,5]], return_groups=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Images\n",
    "\n",
    "Let us generate a random `image` using these lines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0]\n",
      " [0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0]\n",
      " [0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0]\n",
      " [0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0]\n",
      " [0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0]\n",
      " [0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0]\n",
      " [0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0]\n",
      " [0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0]\n",
      " [0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0]\n",
      " [0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0]\n",
      " [0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0]\n",
      " [0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0]\n",
      " [0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0]\n",
      " [0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0]\n",
      " [0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0]\n",
      " [0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0]\n",
      " [0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0]\n",
      " [0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0]\n",
      " [0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0]\n",
      " [0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "x, y = np.meshgrid(np.linspace(-np.pi/2, np.pi/2, 30), np.linspace(-np.pi/2, np.pi/2, 30))\n",
    "image = np.sin(x**2+y**2)[:-1,:-1] > 0.9\n",
    "print(image.astype(int))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "12. Given a binary `image`, return array of its contour positions (pip instal opencv-python instead of conda install)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11  2]\n",
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      " [ 4 15]\n",
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      " [ 5 12]\n",
      " [ 5 11]\n",
      " [ 5 10]\n",
      " [ 6  9]\n",
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      " [ 8  7]\n",
      " [ 9  6]\n",
      " [10  5]\n",
      " [11  5]]\n"
     ]
    }
   ],
   "source": [
    "#converting extracting contours\n",
    "import cv2\n",
    "image = image.astype(np.uint8)\n",
    "contours, hierarchy = cv2.findContours(image, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n",
    "cells = np.vstack(contours).squeeze() #change shape\n",
    "print(cells)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "13. Given a binary image, return locations of all the units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2 11]\n",
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      " [27 17]\n",
      " [27 18]]\n"
     ]
    }
   ],
   "source": [
    "x, y = np.indices(image.shape)\n",
    "mask = image == 1\n",
    "location = np.array([x[mask], y[mask]]).T\n",
    "print(location)\n",
    "\n",
    "#Alternative way 1:\n",
    "\n",
    "from scipy import sparse\n",
    "sparsed_image = sparse.csr_matrix(image)\n",
    "print(np.array(sparsed_image.nonzero()).T)\n",
    "\n",
    "#Alternative way 2:\n",
    "\n",
    "print(np.array(image.nonzero()).T)\n",
    "\n",
    "#Alternative way 3:\n",
    "\n",
    "print(np.array(np.where(image)).T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "14. An image is made of $29 \\times 29$ array. Convert it to a more efficient memory data structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import sparse\n",
    "sparsed_image = sparse.csr_matrix(image) #this is a data structure that stores rows and columns only, 30% of cells only\n",
    "image2 = sparsed_image.todense() #convert back to array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save and retrieve it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9. Save array and retrieve it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.randint(0,10, size=(100,100))\n",
    "np.save(file='data.npy', x)\n",
    "retrieved_array = np.load('data.npy')\n",
    "\n",
    "np.savez_compressed('data.npz', array=x) #not compressed: data.npz\n",
    "retrieved_array = np.load('data.npz')['array']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10. Dowload a huge .npz file with 3 column array straight from github and perform dimensionality reduction on it using `numexpr`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19488504,)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numexpr as ne\n",
    "path = tf.keras.utils.get_file('cubes.npz', 'https://github.com/loijord/lidar_home/raw/master/cubes.npz')\n",
    "cubes = np.load(path)['array']\n",
    "cubes = cubes.astype(np.int64)\n",
    "s0, s1 = cubes[:,0].max()+1, cubes[:,1].max()+1\n",
    "d = {'s0':s0,'s1':s1,'c0':cubes[:,0],'c1':cubes[:,1],'c2':cubes[:,2]}\n",
    "c1D = ne.evaluate('c0+c1*s0+c2*s0*s1',d)\n",
    "print(c1D.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "11. Now you've got `C1D` array, can you show a list of groups as like as you do with `pd.DataFrame(cubes).groupby([0]).indices`? [Source](https://stackoverflow.com/q/59239886/3044825)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Future asks:\n",
    "* [About justification](https://github.com/numpy/numpy/issues/15863)\n",
    "* [Fill missing values according to frequency](https://stackoverflow.com/questions/63548053/)\n",
    "* [Link lists that share common elements](https://stackoverflow.com/questions/63583725/)\n",
    "* [Replace groups of cells by their averages](https://stackoverflow.com/questions/63618938)\n",
    "* [how to replace all the items of array by indexes of specified lists](stackoverflow.com/questions/63623869/) also used in tagging cc in networkx\n",
    "* [`np.isin` in 2D case](https://stackoverflow.com/questions/51352527) [update: it is needed to solve problem in conjunction with product]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More future asks:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Kerneling image\n",
    "* Zoom of image\n",
    "* Visualiser of numpy array dimensions, [like in this source](https://papers.bastolasushan.com/what-are-tensors/), or [this](https://beerensahu.wordpress.com/2018/03/21/pytorch-tutorial-lesson-1-tensor/)\n",
    "* Construct a kernel from given values of its radius.\n",
    "* Interactive (clickable) graphs.\n",
    "* Create graph from 3D sparse matrix, actually pointcloud.\n",
    "* How to intersect 3d matrices faster that it does `numpy` indexed (assuming I understand what is view, ravel, etc.)\n",
    "* Vectorise `[A.index(b) for b in B]`\n",
    "* Review of nxviz library\n",
    "* Review of numpy_indexed library\n",
    "* Study @Divakar's post with `numba` and `np.ravel()` [Source](https://stackoverflow.com/q/59239886/3044825)\n",
    "* Study [answers](https://stackoverflow.com/q/63623869/3044825) of how to replace all the items of array by indexes of specified lists? They contain `np.apply_along_axis`, `np.searchsorted` and similar tricks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A wonderful [explanation](https://towardsdatascience.com/numpy-indexing-explained-c376abb2440d) of `numpy` indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Illustration of 3D arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "# prepare some coordinates\n",
    "x, y, z = np.indices((8, 8, 8))\n",
    "\n",
    "# draw cuboids in the top left and bottom right corners, and a link between them\n",
    "cube1 = (x < 3) & (y < 3) & (z < 3)\n",
    "cube2 = (x >= 5) & (y >= 5) & (z >= 5)\n",
    "link = abs(x - y) + abs(y - z) + abs(z - x) <= 2\n",
    "\n",
    "# combine the objects into a single boolean array\n",
    "voxels = cube1 | cube2 | link\n",
    "\n",
    "# set the colors of each object\n",
    "colors = np.empty(voxels.shape, dtype=object)\n",
    "colors[link] = 'red'\n",
    "colors[cube1] = 'blue'\n",
    "colors[cube2] = 'green'\n",
    "\n",
    "# and plot everything\n",
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "ax.voxels(voxels, facecolors=colors, edgecolor='k')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.arange(60).reshape(3,4,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measuring performance of combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import perfplot\n",
    "from itertools import chain, combinations\n",
    "from scipy.special import comb\n",
    "from scipy.linalg import pascal\n",
    "\n",
    "def C(n, k):  # huge memory overload...\n",
    "    if k == 0:\n",
    "        return np.array([])\n",
    "    if k == 1:\n",
    "        return np.arange(1, n + 1)\n",
    "    elif k == 2:\n",
    "        return np.less.outer(np.arange(n), np.arange(n))\n",
    "    else:\n",
    "        x = C(n, k - 1)\n",
    "        X = np.repeat(x[None, :, :], len(x), axis=0)\n",
    "        Y = np.repeat(x[:, :, None], len(x), axis=2)\n",
    "        return X & Y\n",
    "\n",
    "def C_indices(data):\n",
    "    n, k = data\n",
    "    return np.transpose(np.nonzero(C(n, k)))\n",
    "\n",
    "def comb_index(data):\n",
    "    n, k = data\n",
    "    count = comb(n, k, exact=True)\n",
    "    index = np.fromiter(chain.from_iterable(combinations(range(n), k)),\n",
    "                        int, count=count * k)\n",
    "    return index.reshape(-1, k)\n",
    "\n",
    "def stretch(a, k):\n",
    "    l = a.sum()+len(a)*(-k)\n",
    "    out = np.full(l, -1, dtype=int)\n",
    "    out[0] = a[0]-1\n",
    "    idx = (a-k).cumsum()[:-1]\n",
    "    out[idx] = a[1:]-1-k\n",
    "    return out.cumsum()\n",
    "\n",
    "def numpy_combinations(data):\n",
    "    #print(data)\n",
    "    n, k = data\n",
    "    x = np.array([n])\n",
    "    P = pascal(n).astype(int)\n",
    "    C = []\n",
    "    for b in range(k-1,-1,-1):\n",
    "        x = stretch(x, b)\n",
    "        r = P[b][x - b]\n",
    "        C.append(np.repeat(x, r))\n",
    "    return n - 1 - np.array(C).T\n",
    "\n",
    "def numpy_combinationsv(data):\n",
    "    #print(data)\n",
    "    n, k = data\n",
    "    x = np.array([n])\n",
    "    P = pascal(n).astype(int)\n",
    "    C = []\n",
    "    for b in range(k-1,-1,-1):\n",
    "        x = stretch(x, b)\n",
    "        r = P[b][x - b]\n",
    "        C.append(np.repeat(x, r))\n",
    "    return np.array(C).T\n",
    "\n",
    "def nump(data, i=0):\n",
    "    n, k = data\n",
    "    i = 0\n",
    "    if k == 1:\n",
    "        a = np.arange(i, i+n)\n",
    "        return tuple([a[None, j:] for j in range(n)])\n",
    "    template = nump((n-1, k-1), i+1)\n",
    "    full = np.r_[np.repeat(np.arange(i, i+n-k+1),\n",
    "                           [t.shape[1] for t in template])[None, :],\n",
    "                 np.c_[template]]\n",
    "    return tuple([full[:, j:] for j in np.r_[0, np.add.accumulate(\n",
    "        [t.shape[1] for t in template[:-1]])]])\n",
    "\n",
    "def nump2(data, i=0):\n",
    "    n, k = data\n",
    "    a = np.ones((k, n-k+1), dtype=int)\n",
    "    a[0] = np.arange(n-k+1)\n",
    "    for j in range(1, k):\n",
    "        reps = (n-k+j) - a[j-1]\n",
    "        a = np.repeat(a, reps, axis=1)\n",
    "        ind = np.add.accumulate(reps)\n",
    "        a[j, ind[:-1]] = 1-reps[1:]\n",
    "        a[j, 0] = j\n",
    "        a[j] = np.add.accumulate(a[j])\n",
    "    return a\n",
    "\n",
    "\n",
    "def build_args(k):\n",
    "    return {'setup': lambda x: (k, x),\n",
    "            'kernels': [numpy_combinations, numpy_combinationsv, nump, nump2],\n",
    "            'n_range': [x for x in range(1, k)],\n",
    "            'xlabel': f'N',\n",
    "            'title': f'test of case C({k}, k)',\n",
    "            'show_progress': True,\n",
    "            'equality_check': False}\n",
    "\n",
    "outs = [perfplot.bench(**build_args(n)) for n in (12, 15, 17, 20)]\n",
    "fig = plt.figure(figsize=(20, 20))\n",
    "for i in range(len(outs)):\n",
    "    ax = fig.add_subplot(2, 2, i + 1)\n",
    "    ax.grid(True, which=\"both\")\n",
    "    outs[i].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [0, 1, 3],\n",
       "       [0, 1, 4],\n",
       "       [0, 2, 3],\n",
       "       [0, 2, 4],\n",
       "       [0, 3, 4],\n",
       "       [1, 2, 3],\n",
       "       [1, 2, 4],\n",
       "       [1, 3, 4],\n",
       "       [2, 3, 4]], dtype=int32)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def stretch(a, k):\n",
    "    l = a.sum()+len(a)*(-k)\n",
    "    out = np.full(l, -1, dtype=int)\n",
    "    out[0] = a[0]-1\n",
    "    idx = (a-k).cumsum()[:-1]\n",
    "    out[idx] = a[1:]-1-k\n",
    "    return out.cumsum()\n",
    "\n",
    "def numpy_combinations(data):\n",
    "    #print(data)\n",
    "    n, k = data\n",
    "    x = np.array([n])\n",
    "    P = pascal(n).astype(int)\n",
    "    C = []\n",
    "    for b in range(k-1,-1,-1):\n",
    "        x = stretch(x, b)\n",
    "        r = P[b][x - b]\n",
    "        C.append(np.repeat(x, r))\n",
    "    return n - 1 - np.array(C).T\n",
    "\n",
    "numpy_combinations((5,3))"
   ]
  }
 ],
 "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}