Ćwiczenia/2. Ćwiczenia/Zadanie 1a (Merge Sort - złączanie serii naturalnych).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Proszę zaimplementować algorytm MergeSort sortujący tablicę, opierający się na złączaniu serii naturalnych."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wstępne wyjaśnienia"
   ]
  },
  {
   "attachments": {
    "image-3.png": {
     "image/png": 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"
    },
    "image-4.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seria naturalna to spójny ciąg liczbowy, który spełnia poniższą własność.\n",
    "![image-3.png](attachment:image-3.png)\n",
    "Jako serię traktujemy ciąg rosnący liczb naturalnych. Poniżej znajduje się opis algorytmu.\n",
    "![image-4.png](attachment:image-4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plan działania"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "W poniższej implementacji algotyrmu sortowania poprzez scalanie serii naturalnych, zamiast używać dwóch tablic (list), które imitowałyby 2 pliki robocze oraz tablicy, odpowiadającej plikowi wyjścia, skorzystamy z kolejki (zaimplementowanej, przy pomocy listy jednokierunkowej). Dopóki w kolejce będzie 2 lub więcej elementów, będziemy przeprowadzać operacje scalania."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Funkcja testująca poprawność algorytmu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "def test_sort(sorting_fn, *, samples=20, in_place=True, failed_only=False):\n",
    "    passed = 0\n",
    "    for i in range(samples):\n",
    "        random_lst = [random.randint(-100, 100) for _ in range(random.randint(0, 50))]\n",
    "        random_lst_before = random_lst[:]\n",
    "        expected = sorted(random_lst)\n",
    "        output = sorting_fn(random_lst)\n",
    "        if not in_place:\n",
    "            random_lst = output\n",
    "        is_correct = random_lst == expected\n",
    "        passed += is_correct\n",
    "        if not failed_only or (failed_only and not is_correct):\n",
    "            print(f'TEST #{i+1}:')\n",
    "            print(f'Before sorting: {random_lst}')\n",
    "            print(f'After sorting: {random_lst}')\n",
    "            print(f'Expected result: {expected}')\n",
    "            print(f'Test {\"PASSED\" if is_correct else \"FAILED\"}')\n",
    "            print(f'Current passed-to-tested ratio: {passed}/{i+1}')\n",
    "            print()\n",
    "    print(f'Sorting algorithms is {\"correct\" if passed == samples else \"wrong\"}')\n",
    "    print(f'Passed tests in total: {passed}/{samples}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementacja struktury (kolejki)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Node:\n",
    "    def __init__(self, val):\n",
    "        self.val = val\n",
    "        self.next = None\n",
    "        \n",
    "        \n",
    "class Queue:\n",
    "    def __init__(self, values=None):\n",
    "        self.head = self.tail = None\n",
    "        self.length = 0\n",
    "        if values: self.enqueue_many(values)\n",
    "            \n",
    "    def __len__(self):\n",
    "        return self.length\n",
    "            \n",
    "    def __iter__(self):\n",
    "        curr = self.head\n",
    "        while curr:\n",
    "            yield curr.val\n",
    "            curr = curr.next\n",
    "            \n",
    "    def __str__(self):\n",
    "        # Arrows starts fromthe first element of the queue and are facing the last item\n",
    "        return ' -> '.join(str(v) for v in self)\n",
    "    \n",
    "    def enqueue(self, val):\n",
    "        node = Node(val)\n",
    "        if not self:\n",
    "            self.head = self.tail = node\n",
    "        else:\n",
    "            self.tail.next = node\n",
    "            self.tail = node\n",
    "        self.length += 1\n",
    "        \n",
    "    def enqueue_many(self, values):\n",
    "        for val in values:\n",
    "            self.enqueue(val)\n",
    "        \n",
    "    def dequeue(self):\n",
    "        if not self:\n",
    "            raise IndexError(f'dequeue from empty {self.__class__.__name__}')\n",
    "        removed = self.head.val\n",
    "        if len(self) == 1:\n",
    "            self.head = self.tail = None\n",
    "        else:\n",
    "            self.head = self.head.next\n",
    "        self.length -= 1\n",
    "        return removed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementacja algorytmu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge_series_sort(arr):\n",
    "    series_queue = create_series_queue(arr)\n",
    "    \n",
    "    while len(series_queue) >= 2:\n",
    "        series1 = series_queue.dequeue()\n",
    "        series2 = series_queue.dequeue()\n",
    "        series_queue.enqueue(merge_series(series1, series2))\n",
    "        \n",
    "    return series_queue.dequeue()\n",
    "\n",
    "    \n",
    "def create_series_queue(arr):\n",
    "    if len(arr) <= 1: return Queue([arr])\n",
    "    \n",
    "    res = Queue()\n",
    "    curr = [arr[0]]\n",
    "    for i in range(1, len(arr)):\n",
    "        if arr[i] >= curr[-1]:\n",
    "            curr.append(arr[i])\n",
    "        else:\n",
    "            res.enqueue(curr)\n",
    "            curr = [arr[i]]\n",
    "    \n",
    "    if curr: res.enqueue(curr)\n",
    "    \n",
    "    return res\n",
    "\n",
    "\n",
    "def merge_series(series1, series2):\n",
    "    merged = []\n",
    "    i = j = 0\n",
    "    \n",
    "    while i < len(series1) and j < len(series2):\n",
    "        if series1[i] < series2[j]:\n",
    "            merged.append(series1[i])\n",
    "            i += 1\n",
    "        else:\n",
    "            merged.append(series2[j])\n",
    "            j += 1\n",
    "            \n",
    "    for i in range(i, len(series1)):\n",
    "        merged.append(series1[i])\n",
    "        \n",
    "    for j in range(j, len(series2)):\n",
    "        merged.append(series2[j])\n",
    "        \n",
    "    return merged"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Kilka testów"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TEST #1:\n",
      "Before sorting: [-93, -83, -77, -73, -72, -70, -70, -65, -64, -60, -58, -53, -44, -41, -26, -18, -18, -18, -13, -9, 1, 4, 5, 8, 9, 17, 21, 21, 24, 31, 32, 32, 39, 40, 49, 50, 59, 60, 62, 63, 65, 66, 83, 86, 90, 91, 91, 96, 99]\n",
      "After sorting: [-93, -83, -77, -73, -72, -70, -70, -65, -64, -60, -58, -53, -44, -41, -26, -18, -18, -18, -13, -9, 1, 4, 5, 8, 9, 17, 21, 21, 24, 31, 32, 32, 39, 40, 49, 50, 59, 60, 62, 63, 65, 66, 83, 86, 90, 91, 91, 96, 99]\n",
      "Expected result: [-93, -83, -77, -73, -72, -70, -70, -65, -64, -60, -58, -53, -44, -41, -26, -18, -18, -18, -13, -9, 1, 4, 5, 8, 9, 17, 21, 21, 24, 31, 32, 32, 39, 40, 49, 50, 59, 60, 62, 63, 65, 66, 83, 86, 90, 91, 91, 96, 99]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 1/1\n",
      "\n",
      "TEST #2:\n",
      "Before sorting: [-99, -96, -81, -74, -61, -42, -41, -33, -22, -5, 16, 25, 25, 42, 66, 84, 92, 98, 99]\n",
      "After sorting: [-99, -96, -81, -74, -61, -42, -41, -33, -22, -5, 16, 25, 25, 42, 66, 84, 92, 98, 99]\n",
      "Expected result: [-99, -96, -81, -74, -61, -42, -41, -33, -22, -5, 16, 25, 25, 42, 66, 84, 92, 98, 99]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 2/2\n",
      "\n",
      "TEST #3:\n",
      "Before sorting: [-87, -81, -78, -77, -75, -61, -56, -55, -51, -50, -42, -41, -32, -28, -25, -14, -10, -7, -4, 2, 10, 12, 17, 21, 23, 28, 29, 32, 36, 36, 38, 56, 75, 75, 83, 86, 88, 89, 90, 94, 100]\n",
      "After sorting: [-87, -81, -78, -77, -75, -61, -56, -55, -51, -50, -42, -41, -32, -28, -25, -14, -10, -7, -4, 2, 10, 12, 17, 21, 23, 28, 29, 32, 36, 36, 38, 56, 75, 75, 83, 86, 88, 89, 90, 94, 100]\n",
      "Expected result: [-87, -81, -78, -77, -75, -61, -56, -55, -51, -50, -42, -41, -32, -28, -25, -14, -10, -7, -4, 2, 10, 12, 17, 21, 23, 28, 29, 32, 36, 36, 38, 56, 75, 75, 83, 86, 88, 89, 90, 94, 100]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 3/3\n",
      "\n",
      "TEST #4:\n",
      "Before sorting: [-89, -88, -87, -83, -80, -59, -45, -44, -41, -38, -35, -22, -19, -15, -11, -8, 15, 16, 19, 22, 23, 26, 29, 36, 38, 48, 58, 64, 67, 68, 73, 83, 83, 84, 90, 97, 97, 99]\n",
      "After sorting: [-89, -88, -87, -83, -80, -59, -45, -44, -41, -38, -35, -22, -19, -15, -11, -8, 15, 16, 19, 22, 23, 26, 29, 36, 38, 48, 58, 64, 67, 68, 73, 83, 83, 84, 90, 97, 97, 99]\n",
      "Expected result: [-89, -88, -87, -83, -80, -59, -45, -44, -41, -38, -35, -22, -19, -15, -11, -8, 15, 16, 19, 22, 23, 26, 29, 36, 38, 48, 58, 64, 67, 68, 73, 83, 83, 84, 90, 97, 97, 99]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 4/4\n",
      "\n",
      "TEST #5:\n",
      "Before sorting: [-97, -94, -89, -82, -78, -76, -69, -65, -62, -55, -53, -50, -41, -36, -36, -30, -28, -25, -21, -20, -8, -6, -1, 0, 0, 3, 5, 7, 13, 15, 23, 37, 38, 45, 46, 47, 49, 50, 56, 58, 59, 62, 66, 69, 73, 76, 86, 93, 95, 100]\n",
      "After sorting: [-97, -94, -89, -82, -78, -76, -69, -65, -62, -55, -53, -50, -41, -36, -36, -30, -28, -25, -21, -20, -8, -6, -1, 0, 0, 3, 5, 7, 13, 15, 23, 37, 38, 45, 46, 47, 49, 50, 56, 58, 59, 62, 66, 69, 73, 76, 86, 93, 95, 100]\n",
      "Expected result: [-97, -94, -89, -82, -78, -76, -69, -65, -62, -55, -53, -50, -41, -36, -36, -30, -28, -25, -21, -20, -8, -6, -1, 0, 0, 3, 5, 7, 13, 15, 23, 37, 38, 45, 46, 47, 49, 50, 56, 58, 59, 62, 66, 69, 73, 76, 86, 93, 95, 100]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 5/5\n",
      "\n",
      "TEST #6:\n",
      "Before sorting: [-97, -90, -88, -82, -80, -73, -70, -68, -65, -62, -49, -48, -43, -40, -32, -31, -30, -26, -25, -13, -12, -7, -7, -5, -5, -2, 1, 16, 20, 31, 34, 34, 40, 45, 47, 49, 51, 61, 63, 64, 66, 79, 80, 91, 95, 97, 98, 100]\n",
      "After sorting: [-97, -90, -88, -82, -80, -73, -70, -68, -65, -62, -49, -48, -43, -40, -32, -31, -30, -26, -25, -13, -12, -7, -7, -5, -5, -2, 1, 16, 20, 31, 34, 34, 40, 45, 47, 49, 51, 61, 63, 64, 66, 79, 80, 91, 95, 97, 98, 100]\n",
      "Expected result: [-97, -90, -88, -82, -80, -73, -70, -68, -65, -62, -49, -48, -43, -40, -32, -31, -30, -26, -25, -13, -12, -7, -7, -5, -5, -2, 1, 16, 20, 31, 34, 34, 40, 45, 47, 49, 51, 61, 63, 64, 66, 79, 80, 91, 95, 97, 98, 100]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 6/6\n",
      "\n",
      "TEST #7:\n",
      "Before sorting: [-74, -72, -44, -35, -25, -4, -3, 13, 16, 30, 42, 56, 68, 79, 90]\n",
      "After sorting: [-74, -72, -44, -35, -25, -4, -3, 13, 16, 30, 42, 56, 68, 79, 90]\n",
      "Expected result: [-74, -72, -44, -35, -25, -4, -3, 13, 16, 30, 42, 56, 68, 79, 90]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 7/7\n",
      "\n",
      "TEST #8:\n",
      "Before sorting: [-94, -86, -57, -16, -1, 1, 10, 14, 47, 52, 54, 57, 91, 93, 96]\n",
      "After sorting: [-94, -86, -57, -16, -1, 1, 10, 14, 47, 52, 54, 57, 91, 93, 96]\n",
      "Expected result: [-94, -86, -57, -16, -1, 1, 10, 14, 47, 52, 54, 57, 91, 93, 96]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 8/8\n",
      "\n",
      "TEST #9:\n",
      "Before sorting: [-59, -51, -24, 6, 42, 73]\n",
      "After sorting: [-59, -51, -24, 6, 42, 73]\n",
      "Expected result: [-59, -51, -24, 6, 42, 73]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 9/9\n",
      "\n",
      "TEST #10:\n",
      "Before sorting: [11, 52]\n",
      "After sorting: [11, 52]\n",
      "Expected result: [11, 52]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 10/10\n",
      "\n",
      "TEST #11:\n",
      "Before sorting: [-86, -86, -77, -71, -41, -35, -35, -16, -10, -8, -1, 22, 24, 24, 37, 45, 52, 55, 60, 80, 83, 84, 90, 94]\n",
      "After sorting: [-86, -86, -77, -71, -41, -35, -35, -16, -10, -8, -1, 22, 24, 24, 37, 45, 52, 55, 60, 80, 83, 84, 90, 94]\n",
      "Expected result: [-86, -86, -77, -71, -41, -35, -35, -16, -10, -8, -1, 22, 24, 24, 37, 45, 52, 55, 60, 80, 83, 84, 90, 94]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 11/11\n",
      "\n",
      "TEST #12:\n",
      "Before sorting: [-97, -43, 0, 3, 25, 29, 40, 44, 57, 88]\n",
      "After sorting: [-97, -43, 0, 3, 25, 29, 40, 44, 57, 88]\n",
      "Expected result: [-97, -43, 0, 3, 25, 29, 40, 44, 57, 88]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 12/12\n",
      "\n",
      "TEST #13:\n",
      "Before sorting: [-100, -72, -66, -57, -43, -36, -35, -32, -24, -20, -3, 17, 19, 34, 35, 43, 44, 57, 64, 64, 66, 71, 82, 94, 100]\n",
      "After sorting: [-100, -72, -66, -57, -43, -36, -35, -32, -24, -20, -3, 17, 19, 34, 35, 43, 44, 57, 64, 64, 66, 71, 82, 94, 100]\n",
      "Expected result: [-100, -72, -66, -57, -43, -36, -35, -32, -24, -20, -3, 17, 19, 34, 35, 43, 44, 57, 64, 64, 66, 71, 82, 94, 100]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 13/13\n",
      "\n",
      "TEST #14:\n",
      "Before sorting: [-100, -71, -68, -67, -54, -50, -49, -49, -6, -4, 11, 17, 38, 43, 49, 58, 86, 92, 96]\n",
      "After sorting: [-100, -71, -68, -67, -54, -50, -49, -49, -6, -4, 11, 17, 38, 43, 49, 58, 86, 92, 96]\n",
      "Expected result: [-100, -71, -68, -67, -54, -50, -49, -49, -6, -4, 11, 17, 38, 43, 49, 58, 86, 92, 96]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 14/14\n",
      "\n",
      "TEST #15:\n",
      "Before sorting: [-99, -87, -86, -86, -82, -80, -80, -80, -75, -73, -62, -62, -61, -56, -52, -51, -49, -45, -41, -22, -22, -21, -19, -3, -1, 5, 15, 27, 36, 38, 40, 58, 59, 63, 67, 70, 70, 73, 95, 99, 100]\n",
      "After sorting: [-99, -87, -86, -86, -82, -80, -80, -80, -75, -73, -62, -62, -61, -56, -52, -51, -49, -45, -41, -22, -22, -21, -19, -3, -1, 5, 15, 27, 36, 38, 40, 58, 59, 63, 67, 70, 70, 73, 95, 99, 100]\n",
      "Expected result: [-99, -87, -86, -86, -82, -80, -80, -80, -75, -73, -62, -62, -61, -56, -52, -51, -49, -45, -41, -22, -22, -21, -19, -3, -1, 5, 15, 27, 36, 38, 40, 58, 59, 63, 67, 70, 70, 73, 95, 99, 100]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 15/15\n",
      "\n",
      "TEST #16:\n",
      "Before sorting: [-96, -88, -87, -72, -68, -66, -64, -44, -43, -41, -38, -27, -27, -25, -19, -17, -8, 0, 2, 9, 31, 32, 39, 44, 47, 55, 56, 71, 73, 77, 85, 94]\n",
      "After sorting: [-96, -88, -87, -72, -68, -66, -64, -44, -43, -41, -38, -27, -27, -25, -19, -17, -8, 0, 2, 9, 31, 32, 39, 44, 47, 55, 56, 71, 73, 77, 85, 94]\n",
      "Expected result: [-96, -88, -87, -72, -68, -66, -64, -44, -43, -41, -38, -27, -27, -25, -19, -17, -8, 0, 2, 9, 31, 32, 39, 44, 47, 55, 56, 71, 73, 77, 85, 94]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 16/16\n",
      "\n",
      "TEST #17:\n",
      "Before sorting: [-97, -70, -49, -45, -43, -32, -22, -20, -9, 21, 41, 57, 61, 64, 83, 94]\n",
      "After sorting: [-97, -70, -49, -45, -43, -32, -22, -20, -9, 21, 41, 57, 61, 64, 83, 94]\n",
      "Expected result: [-97, -70, -49, -45, -43, -32, -22, -20, -9, 21, 41, 57, 61, 64, 83, 94]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 17/17\n",
      "\n",
      "TEST #18:\n",
      "Before sorting: [-61, -53, -43, 31, 39, 62, 100]\n",
      "After sorting: [-61, -53, -43, 31, 39, 62, 100]\n",
      "Expected result: [-61, -53, -43, 31, 39, 62, 100]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 18/18\n",
      "\n",
      "TEST #19:\n",
      "Before sorting: [-97, -91, -88, -70, -12, 3, 27, 43, 64, 76, 80, 84, 91, 92, 97]\n",
      "After sorting: [-97, -91, -88, -70, -12, 3, 27, 43, 64, 76, 80, 84, 91, 92, 97]\n",
      "Expected result: [-97, -91, -88, -70, -12, 3, 27, 43, 64, 76, 80, 84, 91, 92, 97]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 19/19\n",
      "\n",
      "TEST #20:\n",
      "Before sorting: [-78, -70, -42, -34, -32, -15, -9, 15, 20, 35, 45, 46, 76, 84, 95, 96]\n",
      "After sorting: [-78, -70, -42, -34, -32, -15, -9, 15, 20, 35, 45, 46, 76, 84, 95, 96]\n",
      "Expected result: [-78, -70, -42, -34, -32, -15, -9, 15, 20, 35, 45, 46, 76, 84, 95, 96]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 20/20\n",
      "\n",
      "TEST #21:\n",
      "Before sorting: [-92, -77, -61, -50, -50, -35, -29, -3, 3, 7, 21, 51, 52, 62, 70, 70, 84, 86, 96]\n",
      "After sorting: [-92, -77, -61, -50, -50, -35, -29, -3, 3, 7, 21, 51, 52, 62, 70, 70, 84, 86, 96]\n",
      "Expected result: [-92, -77, -61, -50, -50, -35, -29, -3, 3, 7, 21, 51, 52, 62, 70, 70, 84, 86, 96]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 21/21\n",
      "\n",
      "TEST #22:\n",
      "Before sorting: [-100, -95, -94, -92, -89, -86, -82, -80, -78, -78, -65, -56, -50, -42, -33, -30, -22, -19, -17, -4, 2, 7, 8, 18, 18, 30, 31, 39, 39, 42, 45, 46, 49, 68, 76, 79, 83, 86, 87, 91, 95]\n",
      "After sorting: [-100, -95, -94, -92, -89, -86, -82, -80, -78, -78, -65, -56, -50, -42, -33, -30, -22, -19, -17, -4, 2, 7, 8, 18, 18, 30, 31, 39, 39, 42, 45, 46, 49, 68, 76, 79, 83, 86, 87, 91, 95]\n",
      "Expected result: [-100, -95, -94, -92, -89, -86, -82, -80, -78, -78, -65, -56, -50, -42, -33, -30, -22, -19, -17, -4, 2, 7, 8, 18, 18, 30, 31, 39, 39, 42, 45, 46, 49, 68, 76, 79, 83, 86, 87, 91, 95]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 22/22\n",
      "\n",
      "TEST #23:\n",
      "Before sorting: [-96, -91, -85, -82, -63, -61, -60, -52, -40, -31, -27, -23, -22, -15, -14, -8, 8, 8, 13, 16, 20, 25, 28, 36, 52, 63, 67, 82, 83, 90, 91, 99]\n",
      "After sorting: [-96, -91, -85, -82, -63, -61, -60, -52, -40, -31, -27, -23, -22, -15, -14, -8, 8, 8, 13, 16, 20, 25, 28, 36, 52, 63, 67, 82, 83, 90, 91, 99]\n",
      "Expected result: [-96, -91, -85, -82, -63, -61, -60, -52, -40, -31, -27, -23, -22, -15, -14, -8, 8, 8, 13, 16, 20, 25, 28, 36, 52, 63, 67, 82, 83, 90, 91, 99]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 23/23\n",
      "\n",
      "TEST #24:\n",
      "Before sorting: [-83, -71, -20, 36, 41, 75, 90]\n",
      "After sorting: [-83, -71, -20, 36, 41, 75, 90]\n",
      "Expected result: [-83, -71, -20, 36, 41, 75, 90]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 24/24\n",
      "\n",
      "TEST #25:\n",
      "Before sorting: [-97, -91, -85, -83, -81, -66, -63, -57, -56, -54, -53, -50, -46, -43, -41, -36, -17, -6, -3, -3, 1, 1, 2, 3, 4, 5, 10, 10, 19, 25, 27, 33, 41, 42, 62, 71, 83]\n",
      "After sorting: [-97, -91, -85, -83, -81, -66, -63, -57, -56, -54, -53, -50, -46, -43, -41, -36, -17, -6, -3, -3, 1, 1, 2, 3, 4, 5, 10, 10, 19, 25, 27, 33, 41, 42, 62, 71, 83]\n",
      "Expected result: [-97, -91, -85, -83, -81, -66, -63, -57, -56, -54, -53, -50, -46, -43, -41, -36, -17, -6, -3, -3, 1, 1, 2, 3, 4, 5, 10, 10, 19, 25, 27, 33, 41, 42, 62, 71, 83]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 25/25\n",
      "\n",
      "TEST #26:\n",
      "Before sorting: [-98, -93, -93, -74, -67, -44, -31, -26, -20, -7, 4, 4, 10, 16, 30, 32, 35, 37, 43, 46, 61, 75, 77, 77, 79, 98, 98, 100]\n",
      "After sorting: [-98, -93, -93, -74, -67, -44, -31, -26, -20, -7, 4, 4, 10, 16, 30, 32, 35, 37, 43, 46, 61, 75, 77, 77, 79, 98, 98, 100]\n",
      "Expected result: [-98, -93, -93, -74, -67, -44, -31, -26, -20, -7, 4, 4, 10, 16, 30, 32, 35, 37, 43, 46, 61, 75, 77, 77, 79, 98, 98, 100]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 26/26\n",
      "\n",
      "TEST #27:\n",
      "Before sorting: [-100, -84, -83, -78, -68, -61, -57, -57, -53, -50, -37, -35, -34, -12, -10, -10, -4, 1, 1, 15, 19, 21, 24, 25, 28, 41, 49, 59, 66, 69, 74, 76, 78, 78, 82, 83, 84, 84, 85, 94]\n",
      "After sorting: [-100, -84, -83, -78, -68, -61, -57, -57, -53, -50, -37, -35, -34, -12, -10, -10, -4, 1, 1, 15, 19, 21, 24, 25, 28, 41, 49, 59, 66, 69, 74, 76, 78, 78, 82, 83, 84, 84, 85, 94]\n",
      "Expected result: [-100, -84, -83, -78, -68, -61, -57, -57, -53, -50, -37, -35, -34, -12, -10, -10, -4, 1, 1, 15, 19, 21, 24, 25, 28, 41, 49, 59, 66, 69, 74, 76, 78, 78, 82, 83, 84, 84, 85, 94]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 27/27\n",
      "\n",
      "TEST #28:\n",
      "Before sorting: [-88, -86, -81, -72, -62, -52, -51, -48, -20, -17, -9, 0, 17, 41, 46, 69, 77, 87]\n",
      "After sorting: [-88, -86, -81, -72, -62, -52, -51, -48, -20, -17, -9, 0, 17, 41, 46, 69, 77, 87]\n",
      "Expected result: [-88, -86, -81, -72, -62, -52, -51, -48, -20, -17, -9, 0, 17, 41, 46, 69, 77, 87]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 28/28\n",
      "\n",
      "TEST #29:\n",
      "Before sorting: [2, 18, 70]\n",
      "After sorting: [2, 18, 70]\n",
      "Expected result: [2, 18, 70]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 29/29\n",
      "\n",
      "TEST #30:\n",
      "Before sorting: [-28, -18, -18, -13, -1, 0, 19, 53]\n",
      "After sorting: [-28, -18, -18, -13, -1, 0, 19, 53]\n",
      "Expected result: [-28, -18, -18, -13, -1, 0, 19, 53]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 30/30\n",
      "\n",
      "TEST #31:\n",
      "Before sorting: [-98, -93, -91, -84, -79, -65, -61, -47, -42, -40, -26, -24, -21, -21, 2, 15, 30, 43, 46, 47, 50, 58, 60, 61, 64, 69, 69, 70, 72, 75, 76, 76, 81, 82, 84, 100]\n",
      "After sorting: [-98, -93, -91, -84, -79, -65, -61, -47, -42, -40, -26, -24, -21, -21, 2, 15, 30, 43, 46, 47, 50, 58, 60, 61, 64, 69, 69, 70, 72, 75, 76, 76, 81, 82, 84, 100]\n",
      "Expected result: [-98, -93, -91, -84, -79, -65, -61, -47, -42, -40, -26, -24, -21, -21, 2, 15, 30, 43, 46, 47, 50, 58, 60, 61, 64, 69, 69, 70, 72, 75, 76, 76, 81, 82, 84, 100]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 31/31\n",
      "\n",
      "TEST #32:\n",
      "Before sorting: [-91, -81, -69, -29, -8, -3, 8, 18, 24, 30, 32, 55, 66, 71, 77, 89, 89]\n",
      "After sorting: [-91, -81, -69, -29, -8, -3, 8, 18, 24, 30, 32, 55, 66, 71, 77, 89, 89]\n",
      "Expected result: [-91, -81, -69, -29, -8, -3, 8, 18, 24, 30, 32, 55, 66, 71, 77, 89, 89]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 32/32\n",
      "\n",
      "TEST #33:\n",
      "Before sorting: [-96, -77, -72, -58, -52, -50, -46, -35, -35, -35, -24, -14, -13, -8, -1, 0, 3, 6, 9, 10, 13, 14, 15, 15, 16, 22, 23, 23, 25, 30, 35, 38, 41, 44, 44, 47, 56, 57, 66, 76, 80, 85, 88, 92, 94, 96, 96, 96, 99]\n",
      "After sorting: [-96, -77, -72, -58, -52, -50, -46, -35, -35, -35, -24, -14, -13, -8, -1, 0, 3, 6, 9, 10, 13, 14, 15, 15, 16, 22, 23, 23, 25, 30, 35, 38, 41, 44, 44, 47, 56, 57, 66, 76, 80, 85, 88, 92, 94, 96, 96, 96, 99]\n",
      "Expected result: [-96, -77, -72, -58, -52, -50, -46, -35, -35, -35, -24, -14, -13, -8, -1, 0, 3, 6, 9, 10, 13, 14, 15, 15, 16, 22, 23, 23, 25, 30, 35, 38, 41, 44, 44, 47, 56, 57, 66, 76, 80, 85, 88, 92, 94, 96, 96, 96, 99]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 33/33\n",
      "\n",
      "TEST #34:\n",
      "Before sorting: [-98, -98, -97, -95, -78, -65, -54, -52, -39, -32, -28, -23, 4, 10, 12, 21, 33, 36, 58, 66, 68, 76, 86, 89]\n",
      "After sorting: [-98, -98, -97, -95, -78, -65, -54, -52, -39, -32, -28, -23, 4, 10, 12, 21, 33, 36, 58, 66, 68, 76, 86, 89]\n",
      "Expected result: [-98, -98, -97, -95, -78, -65, -54, -52, -39, -32, -28, -23, 4, 10, 12, 21, 33, 36, 58, 66, 68, 76, 86, 89]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 34/34\n",
      "\n",
      "TEST #35:\n",
      "Before sorting: [-96, -90, -72, -72, -68, -63, -50, -46, -21, -17, -10, -9, -6, -5, -2, 4, 5, 43, 44, 44, 47, 52, 53, 56, 74, 97]\n",
      "After sorting: [-96, -90, -72, -72, -68, -63, -50, -46, -21, -17, -10, -9, -6, -5, -2, 4, 5, 43, 44, 44, 47, 52, 53, 56, 74, 97]\n",
      "Expected result: [-96, -90, -72, -72, -68, -63, -50, -46, -21, -17, -10, -9, -6, -5, -2, 4, 5, 43, 44, 44, 47, 52, 53, 56, 74, 97]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 35/35\n",
      "\n",
      "TEST #36:\n",
      "Before sorting: [-92, -66, -65, -61, -57, -44, -39, -22, -5, -3, -2, 14, 68, 68, 82, 83, 83]\n",
      "After sorting: [-92, -66, -65, -61, -57, -44, -39, -22, -5, -3, -2, 14, 68, 68, 82, 83, 83]\n",
      "Expected result: [-92, -66, -65, -61, -57, -44, -39, -22, -5, -3, -2, 14, 68, 68, 82, 83, 83]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 36/36\n",
      "\n",
      "TEST #37:\n",
      "Before sorting: [-98, -96, -72, -71, -61, -58, -48, -35, -16, -11, -7, 1, 12, 16, 19, 20, 31, 38, 42, 45, 52, 58, 61, 66, 74, 77, 80, 82, 89]\n",
      "After sorting: [-98, -96, -72, -71, -61, -58, -48, -35, -16, -11, -7, 1, 12, 16, 19, 20, 31, 38, 42, 45, 52, 58, 61, 66, 74, 77, 80, 82, 89]\n",
      "Expected result: [-98, -96, -72, -71, -61, -58, -48, -35, -16, -11, -7, 1, 12, 16, 19, 20, 31, 38, 42, 45, 52, 58, 61, 66, 74, 77, 80, 82, 89]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 37/37\n",
      "\n",
      "TEST #38:\n",
      "Before sorting: [-66, -65, -32, -29, -21, -13, -7, 6, 35, 39, 85, 89, 89, 94]\n",
      "After sorting: [-66, -65, -32, -29, -21, -13, -7, 6, 35, 39, 85, 89, 89, 94]\n",
      "Expected result: [-66, -65, -32, -29, -21, -13, -7, 6, 35, 39, 85, 89, 89, 94]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 38/38\n",
      "\n",
      "TEST #39:\n",
      "Before sorting: [-91, -90, -89, -82, -81, -80, -61, -26, -17, -6, 14, 19, 24, 48, 50, 52, 76]\n",
      "After sorting: [-91, -90, -89, -82, -81, -80, -61, -26, -17, -6, 14, 19, 24, 48, 50, 52, 76]\n",
      "Expected result: [-91, -90, -89, -82, -81, -80, -61, -26, -17, -6, 14, 19, 24, 48, 50, 52, 76]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 39/39\n",
      "\n",
      "TEST #40:\n",
      "Before sorting: [-59, 15]\n",
      "After sorting: [-59, 15]\n",
      "Expected result: [-59, 15]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 40/40\n",
      "\n",
      "TEST #41:\n",
      "Before sorting: [-88, -82, -82, -79, -76, -69, -62, -50, -31, -18, 13, 24, 27, 29, 49, 68, 72]\n",
      "After sorting: [-88, -82, -82, -79, -76, -69, -62, -50, -31, -18, 13, 24, 27, 29, 49, 68, 72]\n",
      "Expected result: [-88, -82, -82, -79, -76, -69, -62, -50, -31, -18, 13, 24, 27, 29, 49, 68, 72]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 41/41\n",
      "\n",
      "TEST #42:\n",
      "Before sorting: [-91, -89, -38, -31, -21, -3, 26, 39, 94]\n",
      "After sorting: [-91, -89, -38, -31, -21, -3, 26, 39, 94]\n",
      "Expected result: [-91, -89, -38, -31, -21, -3, 26, 39, 94]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 42/42\n",
      "\n",
      "TEST #43:\n",
      "Before sorting: [-93, -85, -83, -78, -77, -68, -66, -58, -48, -45, -39, -35, -31, -30, -25, -22, -16, -13, -4, -2, 2, 16, 19, 39, 40, 55, 64, 71]\n",
      "After sorting: [-93, -85, -83, -78, -77, -68, -66, -58, -48, -45, -39, -35, -31, -30, -25, -22, -16, -13, -4, -2, 2, 16, 19, 39, 40, 55, 64, 71]\n",
      "Expected result: [-93, -85, -83, -78, -77, -68, -66, -58, -48, -45, -39, -35, -31, -30, -25, -22, -16, -13, -4, -2, 2, 16, 19, 39, 40, 55, 64, 71]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 43/43\n",
      "\n",
      "TEST #44:\n",
      "Before sorting: [-97, -90, -90, -84, -83, -82, -76, -70, -70, -66, -65, -63, -61, -60, -45, -43, -41, -32, -32, -10, -10, -5, -3, -1, 2, 18, 23, 30, 30, 31, 32, 40, 47, 49, 54, 55, 55, 56, 59, 59, 64, 64, 74, 83, 89, 90, 92, 93, 93, 99]\n",
      "After sorting: [-97, -90, -90, -84, -83, -82, -76, -70, -70, -66, -65, -63, -61, -60, -45, -43, -41, -32, -32, -10, -10, -5, -3, -1, 2, 18, 23, 30, 30, 31, 32, 40, 47, 49, 54, 55, 55, 56, 59, 59, 64, 64, 74, 83, 89, 90, 92, 93, 93, 99]\n",
      "Expected result: [-97, -90, -90, -84, -83, -82, -76, -70, -70, -66, -65, -63, -61, -60, -45, -43, -41, -32, -32, -10, -10, -5, -3, -1, 2, 18, 23, 30, 30, 31, 32, 40, 47, 49, 54, 55, 55, 56, 59, 59, 64, 64, 74, 83, 89, 90, 92, 93, 93, 99]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 44/44\n",
      "\n",
      "TEST #45:\n",
      "Before sorting: [-98, -90, -86, -82, -77, -76, -76, -75, -69, -48, -33, -29, -16, -16, -11, -7, -3, -1, 0, 5, 5, 16, 19, 23, 23, 25, 42, 59, 70, 72, 74, 83, 87, 88, 90, 94]\n",
      "After sorting: [-98, -90, -86, -82, -77, -76, -76, -75, -69, -48, -33, -29, -16, -16, -11, -7, -3, -1, 0, 5, 5, 16, 19, 23, 23, 25, 42, 59, 70, 72, 74, 83, 87, 88, 90, 94]\n",
      "Expected result: [-98, -90, -86, -82, -77, -76, -76, -75, -69, -48, -33, -29, -16, -16, -11, -7, -3, -1, 0, 5, 5, 16, 19, 23, 23, 25, 42, 59, 70, 72, 74, 83, 87, 88, 90, 94]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 45/45\n",
      "\n",
      "TEST #46:\n",
      "Before sorting: [-97, -89, -87, -86, -86, -85, -85, -82, -77, -76, -66, -64, -59, -56, -54, -53, -47, -26, -25, -13, -10, 3, 5, 11, 24, 26, 26, 27, 29, 32, 35, 46, 47, 59, 60, 65, 68, 84, 87, 88, 92, 100]\n",
      "After sorting: [-97, -89, -87, -86, -86, -85, -85, -82, -77, -76, -66, -64, -59, -56, -54, -53, -47, -26, -25, -13, -10, 3, 5, 11, 24, 26, 26, 27, 29, 32, 35, 46, 47, 59, 60, 65, 68, 84, 87, 88, 92, 100]\n",
      "Expected result: [-97, -89, -87, -86, -86, -85, -85, -82, -77, -76, -66, -64, -59, -56, -54, -53, -47, -26, -25, -13, -10, 3, 5, 11, 24, 26, 26, 27, 29, 32, 35, 46, 47, 59, 60, 65, 68, 84, 87, 88, 92, 100]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 46/46\n",
      "\n",
      "TEST #47:\n",
      "Before sorting: [-95, -95, -95, -81, -79, -78, -77, -62, -60, -51, -51, -49, -47, -36, -36, -24, -19, -19, -13, -7, -6, -4, 3, 5, 8, 16, 29, 34, 39, 39, 54, 57, 63, 65, 66, 69, 73, 75, 80, 82, 85, 92, 93]\n",
      "After sorting: [-95, -95, -95, -81, -79, -78, -77, -62, -60, -51, -51, -49, -47, -36, -36, -24, -19, -19, -13, -7, -6, -4, 3, 5, 8, 16, 29, 34, 39, 39, 54, 57, 63, 65, 66, 69, 73, 75, 80, 82, 85, 92, 93]\n",
      "Expected result: [-95, -95, -95, -81, -79, -78, -77, -62, -60, -51, -51, -49, -47, -36, -36, -24, -19, -19, -13, -7, -6, -4, 3, 5, 8, 16, 29, 34, 39, 39, 54, 57, 63, 65, 66, 69, 73, 75, 80, 82, 85, 92, 93]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 47/47\n",
      "\n",
      "TEST #48:\n",
      "Before sorting: [-92, -85, -74, -65, -60, -50, -23, -15, -9, -2, 7, 17, 22, 36, 39, 40, 53, 74, 77]\n",
      "After sorting: [-92, -85, -74, -65, -60, -50, -23, -15, -9, -2, 7, 17, 22, 36, 39, 40, 53, 74, 77]\n",
      "Expected result: [-92, -85, -74, -65, -60, -50, -23, -15, -9, -2, 7, 17, 22, 36, 39, 40, 53, 74, 77]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 48/48\n",
      "\n",
      "TEST #49:\n",
      "Before sorting: [-84, -37, -34, -31, -25, -20, -15, -13, -2, 1, 10, 11, 22, 22, 47, 48, 59, 83, 83, 89, 97]\n",
      "After sorting: [-84, -37, -34, -31, -25, -20, -15, -13, -2, 1, 10, 11, 22, 22, 47, 48, 59, 83, 83, 89, 97]\n",
      "Expected result: [-84, -37, -34, -31, -25, -20, -15, -13, -2, 1, 10, 11, 22, 22, 47, 48, 59, 83, 83, 89, 97]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 49/49\n",
      "\n",
      "TEST #50:\n",
      "Before sorting: [-95, -93, -83, -80, -78, -64, -32, -7, 2, 20, 33, 44, 45, 49, 67, 78, 85, 89, 97]\n",
      "After sorting: [-95, -93, -83, -80, -78, -64, -32, -7, 2, 20, 33, 44, 45, 49, 67, 78, 85, 89, 97]\n",
      "Expected result: [-95, -93, -83, -80, -78, -64, -32, -7, 2, 20, 33, 44, 45, 49, 67, 78, 85, 89, 97]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 50/50\n",
      "\n",
      "TEST #51:\n",
      "Before sorting: []\n",
      "After sorting: []\n",
      "Expected result: []\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 51/51\n",
      "\n",
      "TEST #52:\n",
      "Before sorting: [-90, -88, -85, -68, -63, -58, -38, -36, -31, -27, -13, -13, -5, 2, 12, 24, 36, 37, 40, 52, 56, 66, 71, 82, 92, 93]\n",
      "After sorting: [-90, -88, -85, -68, -63, -58, -38, -36, -31, -27, -13, -13, -5, 2, 12, 24, 36, 37, 40, 52, 56, 66, 71, 82, 92, 93]\n",
      "Expected result: [-90, -88, -85, -68, -63, -58, -38, -36, -31, -27, -13, -13, -5, 2, 12, 24, 36, 37, 40, 52, 56, 66, 71, 82, 92, 93]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 52/52\n",
      "\n",
      "TEST #53:\n",
      "Before sorting: [-87, -53, -50, -8, 20, 71, 95]\n",
      "After sorting: [-87, -53, -50, -8, 20, 71, 95]\n",
      "Expected result: [-87, -53, -50, -8, 20, 71, 95]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 53/53\n",
      "\n",
      "TEST #54:\n",
      "Before sorting: [-98, -93, -88, -85, -84, -82, -80, -73, -66, -62, -62, -60, -55, -51, -51, -43, -42, -39, -35, -25, -15, -12, -5, 1, 2, 5, 9, 22, 28, 30, 36, 38, 40, 40, 47, 54, 56, 61, 67, 70, 82, 83, 91, 95, 99]\n",
      "After sorting: [-98, -93, -88, -85, -84, -82, -80, -73, -66, -62, -62, -60, -55, -51, -51, -43, -42, -39, -35, -25, -15, -12, -5, 1, 2, 5, 9, 22, 28, 30, 36, 38, 40, 40, 47, 54, 56, 61, 67, 70, 82, 83, 91, 95, 99]\n",
      "Expected result: [-98, -93, -88, -85, -84, -82, -80, -73, -66, -62, -62, -60, -55, -51, -51, -43, -42, -39, -35, -25, -15, -12, -5, 1, 2, 5, 9, 22, 28, 30, 36, 38, 40, 40, 47, 54, 56, 61, 67, 70, 82, 83, 91, 95, 99]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 54/54\n",
      "\n",
      "TEST #55:\n",
      "Before sorting: [-97, -96, -95, -85, -78, -69, -58, -55, -49, -46, -41, -23, -21, -16, -2, 3, 6, 20, 27, 32, 39, 45, 49, 52, 53, 55, 67, 70, 80, 88, 92, 94, 96, 97]\n",
      "After sorting: [-97, -96, -95, -85, -78, -69, -58, -55, -49, -46, -41, -23, -21, -16, -2, 3, 6, 20, 27, 32, 39, 45, 49, 52, 53, 55, 67, 70, 80, 88, 92, 94, 96, 97]\n",
      "Expected result: [-97, -96, -95, -85, -78, -69, -58, -55, -49, -46, -41, -23, -21, -16, -2, 3, 6, 20, 27, 32, 39, 45, 49, 52, 53, 55, 67, 70, 80, 88, 92, 94, 96, 97]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 55/55\n",
      "\n",
      "TEST #56:\n",
      "Before sorting: [-87, -56, -54, -53, -46, -42, -40, -38, -31, -21, -16, -13, -9, -8, -3, -2, 4, 6, 12, 21, 25, 29, 30, 34, 36, 39, 40, 55, 72, 82, 100]\n",
      "After sorting: [-87, -56, -54, -53, -46, -42, -40, -38, -31, -21, -16, -13, -9, -8, -3, -2, 4, 6, 12, 21, 25, 29, 30, 34, 36, 39, 40, 55, 72, 82, 100]\n",
      "Expected result: [-87, -56, -54, -53, -46, -42, -40, -38, -31, -21, -16, -13, -9, -8, -3, -2, 4, 6, 12, 21, 25, 29, 30, 34, 36, 39, 40, 55, 72, 82, 100]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 56/56\n",
      "\n",
      "TEST #57:\n",
      "Before sorting: [-96, -95, -94, -88, -86, -82, -81, -79, -79, -72, -65, -63, -59, -54, -52, -51, -50, -43, -37, -35, -28, -22, -18, -13, -8, -7, -4, 0, 2, 6, 10, 24, 25, 35, 35, 35, 43, 45, 46, 64, 68, 69, 74, 76, 88, 91, 98]\n",
      "After sorting: [-96, -95, -94, -88, -86, -82, -81, -79, -79, -72, -65, -63, -59, -54, -52, -51, -50, -43, -37, -35, -28, -22, -18, -13, -8, -7, -4, 0, 2, 6, 10, 24, 25, 35, 35, 35, 43, 45, 46, 64, 68, 69, 74, 76, 88, 91, 98]\n",
      "Expected result: [-96, -95, -94, -88, -86, -82, -81, -79, -79, -72, -65, -63, -59, -54, -52, -51, -50, -43, -37, -35, -28, -22, -18, -13, -8, -7, -4, 0, 2, 6, 10, 24, 25, 35, 35, 35, 43, 45, 46, 64, 68, 69, 74, 76, 88, 91, 98]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 57/57\n",
      "\n",
      "TEST #58:\n",
      "Before sorting: [-100, -85, -71, -62, -42, -31, -26, 31, 45, 58, 59, 70, 74, 82]\n",
      "After sorting: [-100, -85, -71, -62, -42, -31, -26, 31, 45, 58, 59, 70, 74, 82]\n",
      "Expected result: [-100, -85, -71, -62, -42, -31, -26, 31, 45, 58, 59, 70, 74, 82]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 58/58\n",
      "\n",
      "TEST #59:\n",
      "Before sorting: [2, 34, 85]\n",
      "After sorting: [2, 34, 85]\n",
      "Expected result: [2, 34, 85]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 59/59\n",
      "\n",
      "TEST #60:\n",
      "Before sorting: [-88, -88, -65, -57, -57, -54, -36, -35, -28, -20, -4, 4, 33, 33, 49, 77, 81, 82, 99]\n",
      "After sorting: [-88, -88, -65, -57, -57, -54, -36, -35, -28, -20, -4, 4, 33, 33, 49, 77, 81, 82, 99]\n",
      "Expected result: [-88, -88, -65, -57, -57, -54, -36, -35, -28, -20, -4, 4, 33, 33, 49, 77, 81, 82, 99]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 60/60\n",
      "\n",
      "TEST #61:\n",
      "Before sorting: [-71, -68, -54, -53, -42, -32, -26, -24, 7, 12, 28, 29, 34, 51, 60, 60]\n",
      "After sorting: [-71, -68, -54, -53, -42, -32, -26, -24, 7, 12, 28, 29, 34, 51, 60, 60]\n",
      "Expected result: [-71, -68, -54, -53, -42, -32, -26, -24, 7, 12, 28, 29, 34, 51, 60, 60]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 61/61\n",
      "\n",
      "TEST #62:\n",
      "Before sorting: [-93, -78, -75, -70, -58, -56, -26, -3, 15, 20, 78, 78, 80]\n",
      "After sorting: [-93, -78, -75, -70, -58, -56, -26, -3, 15, 20, 78, 78, 80]\n",
      "Expected result: [-93, -78, -75, -70, -58, -56, -26, -3, 15, 20, 78, 78, 80]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 62/62\n",
      "\n",
      "TEST #63:\n",
      "Before sorting: [-100, -99, -95, -93, -89, -83, -78, -75, -61, -50, -48, -39, -33, -28, -22, -13, -11, -6, -4, 0, 5, 12, 15, 17, 20, 23, 24, 27, 31, 34, 36, 40, 41, 46, 49, 54, 61, 68, 70, 73, 79, 81, 82, 87, 97, 97]\n",
      "After sorting: [-100, -99, -95, -93, -89, -83, -78, -75, -61, -50, -48, -39, -33, -28, -22, -13, -11, -6, -4, 0, 5, 12, 15, 17, 20, 23, 24, 27, 31, 34, 36, 40, 41, 46, 49, 54, 61, 68, 70, 73, 79, 81, 82, 87, 97, 97]\n",
      "Expected result: [-100, -99, -95, -93, -89, -83, -78, -75, -61, -50, -48, -39, -33, -28, -22, -13, -11, -6, -4, 0, 5, 12, 15, 17, 20, 23, 24, 27, 31, 34, 36, 40, 41, 46, 49, 54, 61, 68, 70, 73, 79, 81, 82, 87, 97, 97]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 63/63\n",
      "\n",
      "TEST #64:\n",
      "Before sorting: [-41, -7]\n",
      "After sorting: [-41, -7]\n",
      "Expected result: [-41, -7]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 64/64\n",
      "\n",
      "TEST #65:\n",
      "Before sorting: [-92, -91, -84, -83, -75, -71, -71, -69, -62, -61, -54, -53, -45, -34, -30, -29, -23, -18, -18, -14, -3, -1, 20, 24, 26, 33, 38, 38, 39, 46, 50, 53, 60, 61, 64, 69, 86, 89, 97, 98]\n",
      "After sorting: [-92, -91, -84, -83, -75, -71, -71, -69, -62, -61, -54, -53, -45, -34, -30, -29, -23, -18, -18, -14, -3, -1, 20, 24, 26, 33, 38, 38, 39, 46, 50, 53, 60, 61, 64, 69, 86, 89, 97, 98]\n",
      "Expected result: [-92, -91, -84, -83, -75, -71, -71, -69, -62, -61, -54, -53, -45, -34, -30, -29, -23, -18, -18, -14, -3, -1, 20, 24, 26, 33, 38, 38, 39, 46, 50, 53, 60, 61, 64, 69, 86, 89, 97, 98]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 65/65\n",
      "\n",
      "TEST #66:\n",
      "Before sorting: [-100, -94, -93, -91, -91, -90, -89, -86, -61, -52, -44, -41, -33, -31, -9, 2, 3, 3, 6, 7, 11, 15, 28, 32, 42, 46, 58, 68, 69, 73, 76, 81, 86, 90, 92, 99]\n",
      "After sorting: [-100, -94, -93, -91, -91, -90, -89, -86, -61, -52, -44, -41, -33, -31, -9, 2, 3, 3, 6, 7, 11, 15, 28, 32, 42, 46, 58, 68, 69, 73, 76, 81, 86, 90, 92, 99]\n",
      "Expected result: [-100, -94, -93, -91, -91, -90, -89, -86, -61, -52, -44, -41, -33, -31, -9, 2, 3, 3, 6, 7, 11, 15, 28, 32, 42, 46, 58, 68, 69, 73, 76, 81, 86, 90, 92, 99]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 66/66\n",
      "\n",
      "TEST #67:\n",
      "Before sorting: [-52, -51, -30, -23, -14, -7, -7, -5, 2, 13, 21, 36, 39, 40, 57, 82, 93]\n",
      "After sorting: [-52, -51, -30, -23, -14, -7, -7, -5, 2, 13, 21, 36, 39, 40, 57, 82, 93]\n",
      "Expected result: [-52, -51, -30, -23, -14, -7, -7, -5, 2, 13, 21, 36, 39, 40, 57, 82, 93]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 67/67\n",
      "\n",
      "TEST #68:\n",
      "Before sorting: [-96, -93, -87, -80, -78, -50, -21, -14, -8, -7, 3, 9, 34, 47, 57, 58, 62, 74, 76, 79, 79, 83, 92, 96, 98, 100]\n",
      "After sorting: [-96, -93, -87, -80, -78, -50, -21, -14, -8, -7, 3, 9, 34, 47, 57, 58, 62, 74, 76, 79, 79, 83, 92, 96, 98, 100]\n",
      "Expected result: [-96, -93, -87, -80, -78, -50, -21, -14, -8, -7, 3, 9, 34, 47, 57, 58, 62, 74, 76, 79, 79, 83, 92, 96, 98, 100]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 68/68\n",
      "\n",
      "TEST #69:\n",
      "Before sorting: [-97, -94, -87, -87, -83, -81, -65, -64, -63, -60, -56, -55, -43, -15, -1, -1, 5, 7, 7, 7, 11, 12, 15, 15, 16, 16, 20, 35, 53, 53, 57, 59, 61, 64, 68, 70, 73, 80, 87, 96, 97, 97]\n",
      "After sorting: [-97, -94, -87, -87, -83, -81, -65, -64, -63, -60, -56, -55, -43, -15, -1, -1, 5, 7, 7, 7, 11, 12, 15, 15, 16, 16, 20, 35, 53, 53, 57, 59, 61, 64, 68, 70, 73, 80, 87, 96, 97, 97]\n",
      "Expected result: [-97, -94, -87, -87, -83, -81, -65, -64, -63, -60, -56, -55, -43, -15, -1, -1, 5, 7, 7, 7, 11, 12, 15, 15, 16, 16, 20, 35, 53, 53, 57, 59, 61, 64, 68, 70, 73, 80, 87, 96, 97, 97]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 69/69\n",
      "\n",
      "TEST #70:\n",
      "Before sorting: [-98, -87, -83, -71, -66, -52, -48, -44, -43, -25, -24, -15, -10, -6, -2, 0, 8, 10, 15, 17, 28, 28, 48, 48, 49, 53, 54, 56, 62, 65, 70, 72, 73, 74, 83, 87, 92]\n",
      "After sorting: [-98, -87, -83, -71, -66, -52, -48, -44, -43, -25, -24, -15, -10, -6, -2, 0, 8, 10, 15, 17, 28, 28, 48, 48, 49, 53, 54, 56, 62, 65, 70, 72, 73, 74, 83, 87, 92]\n",
      "Expected result: [-98, -87, -83, -71, -66, -52, -48, -44, -43, -25, -24, -15, -10, -6, -2, 0, 8, 10, 15, 17, 28, 28, 48, 48, 49, 53, 54, 56, 62, 65, 70, 72, 73, 74, 83, 87, 92]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 70/70\n",
      "\n",
      "TEST #71:\n",
      "Before sorting: [-59, -54, -52, -35, 2, 6, 21, 29, 97, 98]\n",
      "After sorting: [-59, -54, -52, -35, 2, 6, 21, 29, 97, 98]\n",
      "Expected result: [-59, -54, -52, -35, 2, 6, 21, 29, 97, 98]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 71/71\n",
      "\n",
      "TEST #72:\n",
      "Before sorting: [-78, -77, -77, -75, -69, -63, -57, -51, -31, -3, -3, 8, 11, 30, 30, 41, 48, 48, 63, 79]\n",
      "After sorting: [-78, -77, -77, -75, -69, -63, -57, -51, -31, -3, -3, 8, 11, 30, 30, 41, 48, 48, 63, 79]\n",
      "Expected result: [-78, -77, -77, -75, -69, -63, -57, -51, -31, -3, -3, 8, 11, 30, 30, 41, 48, 48, 63, 79]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 72/72\n",
      "\n",
      "TEST #73:\n",
      "Before sorting: []\n",
      "After sorting: []\n",
      "Expected result: []\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 73/73\n",
      "\n",
      "TEST #74:\n",
      "Before sorting: [-81, -80, -75, -75, -69, -56, -55, -54, -52, -50, -47, -38, -37, -33, -32, -28, -28, -25, -25, -16, -16, -14, -8, -5, 4, 5, 15, 20, 23, 31, 36, 37, 38, 41, 44, 45, 46, 51, 54, 54, 63, 65, 76, 79, 86, 86, 89, 93, 99, 99]\n",
      "After sorting: [-81, -80, -75, -75, -69, -56, -55, -54, -52, -50, -47, -38, -37, -33, -32, -28, -28, -25, -25, -16, -16, -14, -8, -5, 4, 5, 15, 20, 23, 31, 36, 37, 38, 41, 44, 45, 46, 51, 54, 54, 63, 65, 76, 79, 86, 86, 89, 93, 99, 99]\n",
      "Expected result: [-81, -80, -75, -75, -69, -56, -55, -54, -52, -50, -47, -38, -37, -33, -32, -28, -28, -25, -25, -16, -16, -14, -8, -5, 4, 5, 15, 20, 23, 31, 36, 37, 38, 41, 44, 45, 46, 51, 54, 54, 63, 65, 76, 79, 86, 86, 89, 93, 99, 99]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 74/74\n",
      "\n",
      "TEST #75:\n",
      "Before sorting: [-99, -85, -75, -46, -4]\n",
      "After sorting: [-99, -85, -75, -46, -4]\n",
      "Expected result: [-99, -85, -75, -46, -4]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 75/75\n",
      "\n",
      "TEST #76:\n",
      "Before sorting: [-98, -91, -67, -65, -55, -46, 0, 24, 28, 34, 35, 41, 42, 44, 59, 62, 63, 69, 69, 81, 84, 92, 93, 94]\n",
      "After sorting: [-98, -91, -67, -65, -55, -46, 0, 24, 28, 34, 35, 41, 42, 44, 59, 62, 63, 69, 69, 81, 84, 92, 93, 94]\n",
      "Expected result: [-98, -91, -67, -65, -55, -46, 0, 24, 28, 34, 35, 41, 42, 44, 59, 62, 63, 69, 69, 81, 84, 92, 93, 94]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 76/76\n",
      "\n",
      "TEST #77:\n",
      "Before sorting: [-97, -95, -94, -91, -85, -85, -81, -80, -77, -73, -58, -55, -53, -34, -30, -29, -27, -23, -21, -16, -10, -10, 2, 4, 4, 15, 16, 18, 22, 24, 27, 44, 49, 51, 52, 53, 53, 54, 56, 58, 60, 63, 91, 97, 97, 97, 98]\n",
      "After sorting: [-97, -95, -94, -91, -85, -85, -81, -80, -77, -73, -58, -55, -53, -34, -30, -29, -27, -23, -21, -16, -10, -10, 2, 4, 4, 15, 16, 18, 22, 24, 27, 44, 49, 51, 52, 53, 53, 54, 56, 58, 60, 63, 91, 97, 97, 97, 98]\n",
      "Expected result: [-97, -95, -94, -91, -85, -85, -81, -80, -77, -73, -58, -55, -53, -34, -30, -29, -27, -23, -21, -16, -10, -10, 2, 4, 4, 15, 16, 18, 22, 24, 27, 44, 49, 51, 52, 53, 53, 54, 56, 58, 60, 63, 91, 97, 97, 97, 98]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 77/77\n",
      "\n",
      "TEST #78:\n",
      "Before sorting: [-94, -82, -77, -68, -57, -56, -47, -29, -23, -21, -11, -8, -3, 4, 9, 12, 21, 25, 34, 36, 55, 58, 70, 77, 83, 91, 99, 100]\n",
      "After sorting: [-94, -82, -77, -68, -57, -56, -47, -29, -23, -21, -11, -8, -3, 4, 9, 12, 21, 25, 34, 36, 55, 58, 70, 77, 83, 91, 99, 100]\n",
      "Expected result: [-94, -82, -77, -68, -57, -56, -47, -29, -23, -21, -11, -8, -3, 4, 9, 12, 21, 25, 34, 36, 55, 58, 70, 77, 83, 91, 99, 100]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 78/78\n",
      "\n",
      "TEST #79:\n",
      "Before sorting: [-98, -87, -73, -39, -38, -32, -4, 0, 2, 4, 19, 26, 27, 33, 43, 57, 59, 67, 75, 86, 100]\n",
      "After sorting: [-98, -87, -73, -39, -38, -32, -4, 0, 2, 4, 19, 26, 27, 33, 43, 57, 59, 67, 75, 86, 100]\n",
      "Expected result: [-98, -87, -73, -39, -38, -32, -4, 0, 2, 4, 19, 26, 27, 33, 43, 57, 59, 67, 75, 86, 100]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 79/79\n",
      "\n",
      "TEST #80:\n",
      "Before sorting: [-94, -93, -81, -62, -54, -38, -28, -12, 4, 15, 18, 26, 27, 30, 54, 57, 64, 78]\n",
      "After sorting: [-94, -93, -81, -62, -54, -38, -28, -12, 4, 15, 18, 26, 27, 30, 54, 57, 64, 78]\n",
      "Expected result: [-94, -93, -81, -62, -54, -38, -28, -12, 4, 15, 18, 26, 27, 30, 54, 57, 64, 78]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 80/80\n",
      "\n",
      "TEST #81:\n",
      "Before sorting: [-98, -98, -97, -90, -87, -86, -85, -82, -80, -74, -73, -66, -65, -64, -63, -63, -61, -58, -44, -43, -42, -31, -27, -23, -23, -18, -17, -6, -4, 11, 14, 27, 27, 36, 41, 42, 44, 45, 47, 48, 54, 54, 57, 61, 64, 82, 90, 100]\n",
      "After sorting: [-98, -98, -97, -90, -87, -86, -85, -82, -80, -74, -73, -66, -65, -64, -63, -63, -61, -58, -44, -43, -42, -31, -27, -23, -23, -18, -17, -6, -4, 11, 14, 27, 27, 36, 41, 42, 44, 45, 47, 48, 54, 54, 57, 61, 64, 82, 90, 100]\n",
      "Expected result: [-98, -98, -97, -90, -87, -86, -85, -82, -80, -74, -73, -66, -65, -64, -63, -63, -61, -58, -44, -43, -42, -31, -27, -23, -23, -18, -17, -6, -4, 11, 14, 27, 27, 36, 41, 42, 44, 45, 47, 48, 54, 54, 57, 61, 64, 82, 90, 100]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 81/81\n",
      "\n",
      "TEST #82:\n",
      "Before sorting: []\n",
      "After sorting: []\n",
      "Expected result: []\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 82/82\n",
      "\n",
      "TEST #83:\n",
      "Before sorting: [-91, -87, -86, -79, -78, -74, -71, -66, -62, -46, -44, -30, -28, -25, -19, -17, -16, 10, 11, 38, 43, 48, 51, 51, 54, 60, 69, 71, 83, 89, 94]\n",
      "After sorting: [-91, -87, -86, -79, -78, -74, -71, -66, -62, -46, -44, -30, -28, -25, -19, -17, -16, 10, 11, 38, 43, 48, 51, 51, 54, 60, 69, 71, 83, 89, 94]\n",
      "Expected result: [-91, -87, -86, -79, -78, -74, -71, -66, -62, -46, -44, -30, -28, -25, -19, -17, -16, 10, 11, 38, 43, 48, 51, 51, 54, 60, 69, 71, 83, 89, 94]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 83/83\n",
      "\n",
      "TEST #84:\n",
      "Before sorting: [-100, -81, -81, 28, 36, 62, 65, 75, 93]\n",
      "After sorting: [-100, -81, -81, 28, 36, 62, 65, 75, 93]\n",
      "Expected result: [-100, -81, -81, 28, 36, 62, 65, 75, 93]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 84/84\n",
      "\n",
      "TEST #85:\n",
      "Before sorting: [-97, -94, -93, -92, -82, -78, -68, -68, -62, -51, -45, -33, -31, -31, -27, -18, -16, -8, -3, 1, 13, 14, 35, 39, 42, 59, 69, 70, 74, 82, 82, 85, 85, 94, 95, 100]\n",
      "After sorting: [-97, -94, -93, -92, -82, -78, -68, -68, -62, -51, -45, -33, -31, -31, -27, -18, -16, -8, -3, 1, 13, 14, 35, 39, 42, 59, 69, 70, 74, 82, 82, 85, 85, 94, 95, 100]\n",
      "Expected result: [-97, -94, -93, -92, -82, -78, -68, -68, -62, -51, -45, -33, -31, -31, -27, -18, -16, -8, -3, 1, 13, 14, 35, 39, 42, 59, 69, 70, 74, 82, 82, 85, 85, 94, 95, 100]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 85/85\n",
      "\n",
      "TEST #86:\n",
      "Before sorting: [-62, -48, -5, 54, 72, 99]\n",
      "After sorting: [-62, -48, -5, 54, 72, 99]\n",
      "Expected result: [-62, -48, -5, 54, 72, 99]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 86/86\n",
      "\n",
      "TEST #87:\n",
      "Before sorting: [-85, 26]\n",
      "After sorting: [-85, 26]\n",
      "Expected result: [-85, 26]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 87/87\n",
      "\n",
      "TEST #88:\n",
      "Before sorting: [-91, -72, -44, -32, -32, -29, 20, 51, 72, 88, 96]\n",
      "After sorting: [-91, -72, -44, -32, -32, -29, 20, 51, 72, 88, 96]\n",
      "Expected result: [-91, -72, -44, -32, -32, -29, 20, 51, 72, 88, 96]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 88/88\n",
      "\n",
      "TEST #89:\n",
      "Before sorting: [-100, -90, -83, -76, -74, -68, -35, -26, -16, 0, 7, 9, 10, 19, 57, 82, 88, 92]\n",
      "After sorting: [-100, -90, -83, -76, -74, -68, -35, -26, -16, 0, 7, 9, 10, 19, 57, 82, 88, 92]\n",
      "Expected result: [-100, -90, -83, -76, -74, -68, -35, -26, -16, 0, 7, 9, 10, 19, 57, 82, 88, 92]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 89/89\n",
      "\n",
      "TEST #90:\n",
      "Before sorting: [-59, -58, -44, -42, -13, -12, 2, 23, 32, 73, 76, 82]\n",
      "After sorting: [-59, -58, -44, -42, -13, -12, 2, 23, 32, 73, 76, 82]\n",
      "Expected result: [-59, -58, -44, -42, -13, -12, 2, 23, 32, 73, 76, 82]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 90/90\n",
      "\n",
      "TEST #91:\n",
      "Before sorting: [-95, -86]\n",
      "After sorting: [-95, -86]\n",
      "Expected result: [-95, -86]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 91/91\n",
      "\n",
      "TEST #92:\n",
      "Before sorting: [-78, -66, -66, -52, -42, -34, -34, -26, -21, -18, -14, 18, 25, 29, 39, 48, 71, 93, 95, 98, 100]\n",
      "After sorting: [-78, -66, -66, -52, -42, -34, -34, -26, -21, -18, -14, 18, 25, 29, 39, 48, 71, 93, 95, 98, 100]\n",
      "Expected result: [-78, -66, -66, -52, -42, -34, -34, -26, -21, -18, -14, 18, 25, 29, 39, 48, 71, 93, 95, 98, 100]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 92/92\n",
      "\n",
      "TEST #93:\n",
      "Before sorting: [-97, -94, -8, 8, 10, 14, 22, 43, 46, 47]\n",
      "After sorting: [-97, -94, -8, 8, 10, 14, 22, 43, 46, 47]\n",
      "Expected result: [-97, -94, -8, 8, 10, 14, 22, 43, 46, 47]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 93/93\n",
      "\n",
      "TEST #94:\n",
      "Before sorting: [-93, -91, -84, -82, -74, -70, -63, -59, -51, -49, -47, -43, -41, -37, -37, -34, -31, -27, -18, -18, -8, 3, 7, 8, 14, 25, 31, 33, 34, 39, 46, 48, 50, 64, 66, 67, 88, 91, 95, 99]\n",
      "After sorting: [-93, -91, -84, -82, -74, -70, -63, -59, -51, -49, -47, -43, -41, -37, -37, -34, -31, -27, -18, -18, -8, 3, 7, 8, 14, 25, 31, 33, 34, 39, 46, 48, 50, 64, 66, 67, 88, 91, 95, 99]\n",
      "Expected result: [-93, -91, -84, -82, -74, -70, -63, -59, -51, -49, -47, -43, -41, -37, -37, -34, -31, -27, -18, -18, -8, 3, 7, 8, 14, 25, 31, 33, 34, 39, 46, 48, 50, 64, 66, 67, 88, 91, 95, 99]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 94/94\n",
      "\n",
      "TEST #95:\n",
      "Before sorting: [-100, -92, -87, -81, -78, -76, -63, -58, -56, -54, -47, -40, -39, -36, -32, -27, -12, -8, -2, -1, 0, 1, 11, 12, 24, 30, 32, 39, 44, 46, 47, 48, 51, 51, 57, 58, 63, 64, 72, 79, 86, 88, 90, 95, 95]\n",
      "After sorting: [-100, -92, -87, -81, -78, -76, -63, -58, -56, -54, -47, -40, -39, -36, -32, -27, -12, -8, -2, -1, 0, 1, 11, 12, 24, 30, 32, 39, 44, 46, 47, 48, 51, 51, 57, 58, 63, 64, 72, 79, 86, 88, 90, 95, 95]\n",
      "Expected result: [-100, -92, -87, -81, -78, -76, -63, -58, -56, -54, -47, -40, -39, -36, -32, -27, -12, -8, -2, -1, 0, 1, 11, 12, 24, 30, 32, 39, 44, 46, 47, 48, 51, 51, 57, 58, 63, 64, 72, 79, 86, 88, 90, 95, 95]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 95/95\n",
      "\n",
      "TEST #96:\n",
      "Before sorting: [-82, -79, -78, -74, -56, -55, -46, -46, -46, -44, -43, -37, -30, -28, -26, -25, -21, -16, -16, -13, -9, -4, -4, 0, 1, 5, 11, 14, 19, 23, 23, 23, 39, 44, 64, 65, 67, 67, 69, 78, 79, 82, 84, 92, 94]\n",
      "After sorting: [-82, -79, -78, -74, -56, -55, -46, -46, -46, -44, -43, -37, -30, -28, -26, -25, -21, -16, -16, -13, -9, -4, -4, 0, 1, 5, 11, 14, 19, 23, 23, 23, 39, 44, 64, 65, 67, 67, 69, 78, 79, 82, 84, 92, 94]\n",
      "Expected result: [-82, -79, -78, -74, -56, -55, -46, -46, -46, -44, -43, -37, -30, -28, -26, -25, -21, -16, -16, -13, -9, -4, -4, 0, 1, 5, 11, 14, 19, 23, 23, 23, 39, 44, 64, 65, 67, 67, 69, 78, 79, 82, 84, 92, 94]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 96/96\n",
      "\n",
      "TEST #97:\n",
      "Before sorting: [-99, -93, -89, -80, -80, -76, -75, -72, -70, -68, -67, -66, -65, -59, -58, -55, -53, -46, -46, -42, -42, -20, -19, -16, -15, -13, -12, -12, -11, 6, 8, 14, 21, 33, 48, 52, 52, 53, 60, 77, 77, 83, 86, 88, 96, 97, 98, 99]\n",
      "After sorting: [-99, -93, -89, -80, -80, -76, -75, -72, -70, -68, -67, -66, -65, -59, -58, -55, -53, -46, -46, -42, -42, -20, -19, -16, -15, -13, -12, -12, -11, 6, 8, 14, 21, 33, 48, 52, 52, 53, 60, 77, 77, 83, 86, 88, 96, 97, 98, 99]\n",
      "Expected result: [-99, -93, -89, -80, -80, -76, -75, -72, -70, -68, -67, -66, -65, -59, -58, -55, -53, -46, -46, -42, -42, -20, -19, -16, -15, -13, -12, -12, -11, 6, 8, 14, 21, 33, 48, 52, 52, 53, 60, 77, 77, 83, 86, 88, 96, 97, 98, 99]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 97/97\n",
      "\n",
      "TEST #98:\n",
      "Before sorting: [-27]\n",
      "After sorting: [-27]\n",
      "Expected result: [-27]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 98/98\n",
      "\n",
      "TEST #99:\n",
      "Before sorting: [-85, -61, -49, -44, -43, -3, 8, 25, 50, 56, 92]\n",
      "After sorting: [-85, -61, -49, -44, -43, -3, 8, 25, 50, 56, 92]\n",
      "Expected result: [-85, -61, -49, -44, -43, -3, 8, 25, 50, 56, 92]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 99/99\n",
      "\n",
      "TEST #100:\n",
      "Before sorting: [-91, -73, -70, -59, -19, 2, 41, 45, 50, 80]\n",
      "After sorting: [-91, -73, -70, -59, -19, 2, 41, 45, 50, 80]\n",
      "Expected result: [-91, -73, -70, -59, -19, 2, 41, 45, 50, 80]\n",
      "Test PASSED\n",
      "Current passed-to-tested ratio: 100/100\n",
      "\n",
      "Sorting algorithms is correct\n",
      "Passed tests in total: 100/100\n"
     ]
    }
   ],
   "source": [
    "test_sort(merge_series_sort, samples=100, in_place=False)"
   ]
  }
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