Format

graphics.ipynb

@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": null,
    "id": "7fced95c",
    "metadata": {},
    "outputs": [],
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": null,
    "id": "d20bde93",
    "metadata": {},
    "outputs": [],
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": null,
    "id": "d0fe0703",
    "metadata": {},
    "outputs": [],
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": null,
    "id": "18bd929b",
    "metadata": {},
    "outputs": [],
@@ -97,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": null,
    "id": "73525bc7",
    "metadata": {},
    "outputs": [],
@@ -133,31 +133,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": null,
    "id": "b8a772e3",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAHHCAYAAABDUnkqAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjYsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvq6yFwwAAAAlwSFlzAAAPYQAAD2EBqD+naQAAbuRJREFUeJzt3QdYU1cbB/A/Q0AUB24U90Jx71X3buv6tK5qXbXV1q2tVeveWmdrHa22Vmvdtu69956ouHBPBBw4IN/znhgMCAqa5Gb8f89zhdzc3JycRO6bc95zjpNOp9OBiIiIyE44a10AIiIiIlNicENERER2hcENERER2RUGN0RERGRXGNwQERGRXWFwQ0RERHaFwQ0RERHZFQY3REREZFcY3BAREZFdYXBDDuny5ctwcnLCnDlzYM+2bt2qXqf8pISTuhs0aJDWxSCiBGJwQ3ZHAha5KB08eFDropCJ7d69WwUbDx8+1LooNufGjRuq7o4ePQprtGnTJrRt2xa5c+eGp6cnsmfPjvbt2+PmzZtxfhbKly+vjk2fPj26dOmCR48eWbzcZJ1ctS4AkRayZMmCp0+fIlGiRFoXhRJALmiDBw/GF198gRQpUmhdHJsLbqTusmbNisKFC8PafPfdd3jw4AEaN26MXLly4eLFi5g6dSpWrlypAjIJYAzkdtWqVeHn54effvoJ165dw7hx43D+/HmsWbNG09dB1oHBDTkkadnx8PCANXvy5In6VkrkCCRIkZYYZ+fXHQq1atVCxYoVVZAzbNiwqP0//PADUqZMqbpbkyVLpvZJ0NahQwesX78eNWrU0OQ1kPVgtxQ5pNhybqQ1IGnSpLh+/Trq16+vfk+TJg169eqFiIiIaI+PjIzExIkTkT9/fhUkpUuXDh07dkRwcHC041asWIG6devCx8cH7u7uyJEjB4YOHfrG+SpVqgR/f38cOnQIH330kQpq5A/42wQEBOB///sfvL29VRmKFy+Of//9N16vf9++ferCkTx5cvVccgHZtWtXtGOkC0Pq6Ny5c2jZsqU6VupjwIAB0Ol0uHr1KurVq6cuLvKtevz48W88z7NnzzBw4EDkzJlTvX5fX1/06dNH7Tcmz/PNN99g+fLlqh7kWKnbtWvXRitP79691e/ZsmVTj5FN3suEkm6tbt26qfLIc0n5Ro8erd7Xd5HPh3SfyHtuKOfvv/8ea67TwoULVWtJxowZ4eXlpd6vkJAQ9frl+dOmTas+Z23atHmjTsRff/2FYsWKIXHixOp9btq0qar32D47p0+fRuXKldX7Kc83ZsyYaOUpUaKE+l2ey1B3hs+/tHg0atRIvY/yWcqUKZN6Limrpcjn3jiwMeyT133mzJmofaGhodiwYYP6TBoCG9GqVStVl1LnRGy5ITIiQUfNmjVRqlQp1cy9ceNGddGWoOTrr7+OOk4CGbkwyIVC+vovXbqkvl0eOXJEBQmG7i45Rv7g9ujRQ/3cvHkzfvzxR/UHeuzYsdGe+/79+6hdu7a6qMgfbrl4xuXUqVMoV66cuoh9//33SJIkifqjLkHZkiVL0KBBgzgfK2WQ55GLpgQeckGZPXs2qlSpgh07dqBkyZLRjv/ss89U8/+oUaOwatUq9Q1aLjjTp09Xj5GgYN68eSoIlAuoXJCEBAqffvopdu7ciS+//FKd48SJE5gwYYIKmCSQMSbHLV26FJ06dVKBwOTJk9UFNygoCKlSpULDhg3V4/7++291jtSpU6vHScCV0BYxCeYkSJH3MXPmzKq7q2/fviq/Q4LWuNy+fRulS5eOCsbkuaUbpF27duo9lYDF2MiRI1VgIu9RYGAgpkyZoj4bUucSCEvAtnfvXvU5kYBNPhsGw4cPV4FkkyZNVO7J3bt31eOlfuVzZtwtJ+eSYFXqSI5fvHix6uYpUKCAeq+l7ocMGaLOL+9FhQoV1OPKli2L58+fq8+8BFfffvutCnCkbqQ7SIJACWrfVpeyvYuLi4tqaUkoyaGRzfBeC/kMvXz5UgXzxtzc3FR3m9QNkXwDI7Irs2fP1slH+8CBA3Eec+nSJXWMHGvQunVrtW/IkCHRji1SpIiuWLFiUbd37Nihjps3b16049auXfvG/idPnrzx3B07dtR5enrqwsPDo/ZVrFhRPfbXX3+N12usWrWqrkCBAtHOERkZqStbtqwuV65cUfu2bNmizis/DcfI/TVr1lS/G5czW7ZsuurVq0ftGzhwoHrsl19+GbXv5cuXukyZMumcnJx0o0aNitofHBysS5w4sapDg7lz5+qcnZ1VfRmT1yjn3bVrV9Q+ue3m5qYLDAyM2nfs2DG1f8qUKVH7xo4dq/bJ+/e+hg4dqkuSJInu3Llz0fZ///33OhcXF11QUFC0ckk9GLRr106XIUMG3b1796I9tmnTprrkyZNHvd+Gevf399c9f/486rhmzZqpuqtdu3a0x5cpU0aXJUuWqNuXL19WZRk+fHi0406cOKFzdXWNtt/w2fnzzz+j9j179kyXPn16XaNGjaL2yf+HmJ95ceTIEbV/0aJFuoQyfEbetRm/toS+V/L4TZs2Re2Tcsq+7du3v3F848aN1esmYrcUUQxfffVVtNvyLVeSGw0WLVqkvs1Wr14d9+7di9qkJURaZ7Zs2RJ1rHxrNwgLC1PHyfnk2650KxmTLg5pCXoXSbqU1hf5hm44p2zS8iPfwKWLQb55x0YSMeX+5s2bq+MNj338+LFK0Ny+ffsbXTPSamD8DVy+Mct1X1orDKQVIU+ePG/Uk7QY5M2bN1o9SWuPMK4nUa1aNdVCZlCwYEHV7WB8TlOQcsl7IC0JxuWS55eWO6mD2MhrllaxTz75RP1u/Fipd+nCOXz4cLTHSFeJcdK6tAjKY6Vby5jsl+4maZEQ0oIl74O8x8bPI60qkmwbs+7kcyetfcatGNICF5+6M7TMrFu3Ll6tMDFfn3QRvWuTlr2EkvdBuvSkDgyfGSEDAQz/X2KSLjXD/eTY2C1FFOOPY8xuDrkIGufSSHAgFzLJl4jNnTt3onUf9e/fXwUj0m1hLGY+g3QxyUXpXaR7Qy6Q0mUhW1xlkPPFJGUXrVu3jvP8Ui7jLgTptol5MZR6Mu4qMOyXgMn4uSRXIq5uI+N6iu15Yqt7U5ByHT9+PN7lMpBuIemmmTFjhtri89jY6k5Irk/M/RLMSN1LF5yUUd5jCWRiE3OUn+TISFdZzLqT1/ku0h0m3aaS0CtBiAR+0p1oyLN6GxmuLZupSeAvXauSSzRr1qxo9xm+MMSWoxQeHh7tCwU5LgY3REakZeJd5CIkgU1c30YNF025EEpuh7Q+SL6DtEpIUCDf7iUfImYLSXz/KBseJzku0mIQG0mQfdtjJd8nruHA0grwrjqJq570PTmvn0tyPuSiGZuYF/j4nNMUpFzS6iaJzbGReVbiepyQi35cwaG0NsXnNb3rtcpzSbAi+TyxHRuf98j4fO8ieWWSUC8J8DLaSPLIJF9I8oEkcHpXTsy7SPnimxslLVgy2kkCq9WrV6v8K2MZMmRQP2Ob/0b2SfI+EYMbogSSIEUSjSWh920BiYxQkZYM6WIwJNkKST7+EIZvyvLtXbpSEsLQ7SMBV0Ifm1DyXMeOHVPdXTFbFd6XKc4j5ZILckJfv1yc5UIrXVeWqDsJTKRVJa5gy9R1J4GobNLSKAnW8vn+9ddfow3BjkmS7qXrKD7zSsVnVJv8f5HARlplZFI/QyBjTFpzXF1d1SSd0mVlIInR0u1qvI8cF3NuiBJI/njKBU6GdMckOROG2XMN36aNvz3LH+Bffvnlg55fWo1k+K+MVort26t0n8RF8oLkwikXpdi+cb/tse9TT5L7M3PmzDfuk7wIyfNJKBkVJj5khmIp1549e1SOSUxyXkPeS0zyfsroLcm7OXnypFnrTkY9yfNJ4BCz9UVuG3f/fWjdSXdpzNcsQY6M6Iqt68dcOTfyeahTp476zEiLTVxdctKiI8GlDJOXnDODuXPnqs+0TAJIxJYbslsy94jxPCkGXbt2/aDzSleTDCGWZnv5pijfNKUVRfIkJFl10qRJaj4TGWYreQ/ShSHN/PLNWf4Am6Kb5eeff1YTnslFSCYuk9YcGaYsF22ZrVVaTGIjFyzJYZDhwTI/iyQwS26OXFAkSVVadP777z+Ywueff66Gp0uCtpxbWgIkKJR8CtkvwUXM4bzvIsGZ6NevnxoyL/UuCb5y4ZZh1RIMyHNJ8BcXmStH5gP6+OOPVVeMnFMurDLEWIZQSwtDzHwiAxkOL+eXBGCp93z58qkEb+lqlNY8+d0UJACVFhMZni7lkSH+0mokrX7Lli1Tw7mlWzKh55TEb2mNkXNJncnrkM+KDGuXoEBaiSTQkc+pIZizVM5NixYtsH//fpVsLblaxnPbSDec1IHxMHn5/yX/F6Uu5DMvXWvyf1GGxBNxKDjZ7VDwuLarV6/GORRchgjHNdw1phkzZqgh4jIE2svLSw3N7tOnj+7GjRtRx8hw59KlS6tjfHx81P3r1q2LNjzbMJw3f/78CXqdFy5c0LVq1UoNfU2UKJEuY8aMuo8//li3ePHiOIeCGw//bdiwoS5VqlQ6d3d3NVS3SZMm0YbcGl733bt3oz02rnqK7TXIMOjRo0er/fI8KVOmVHU2ePBgXUhISNRx8jydO3d+45xSLuPh5YbhwfJaZZi58bDwnj17qmHWZ86ceWfdhYWF6fr27avLmTOnGoKeOnVqNYx+3Lhx0YZuxxwKLm7fvq3K6uvrq+pd6l+G5svnIWa9xxxeHdc0BXHV9ZIlS3Tly5dX9S1b3rx51XOfPXv2nZ8dqbeYQ7BXrFihy5cvnxpObvj8X7x4Ude2bVtdjhw5dB4eHjpvb29d5cqVdRs3btRZkpQ1IUPJZYoBec+kzGnSpFH1EhoaatEyk/Vykn+0DrCIiD6UDH2W3A5pPSMix8bghohsnuSNSMKvdBPK3DpE5NgY3BAREZFLine truncated
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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 truncated
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "N = 20\n",
     "show_plot_linear(\"matrix_linear.txt\", N)\n",
@@ -174,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": null,
    "id": "e0e9266d",
    "metadata": {},
    "outputs": [],
@@ -190,22 +169,14 @@
     "    real = read_data_from_file(analytical_file)\n",
     "    matrix = read_data_from_file(program_file)\n",
     "    \n",
-    "    # Расчет абсолютных разностей\n",
     "    absolute_errors = np.abs(real - matrix)\n",
-    "\n",
-    "    # Расчет относительных погрешностей (в долях)\n",
-    "    # Добавляем маленькое значение к знаменателю для избежания деления на ноль\n",
     "    relative_errors = absolute_errors / (np.abs(real))\n",
-    "\n",
-    "    # Расчет относительных погрешностей в процентах\n",
     "    relative_errors_percent = relative_errors * 100\n",
     "\n",
-    "    # Нахождение максимальной относительной погрешности\n",
     "    max_relative_error = np.max(relative_errors)\n",
     "    max_relative_error_percent = np.max(relative_errors_percent)\n",
     "    max_error_index = np.argmax(relative_errors)\n",
     "\n",
-    "    # Вывод результатов\n",
     "    print(f\"\\nМаксимальная относительная погрешность: {max_relative_error:.6f} ({max_relative_error_percent:.2f}%)\")\n",
     "    print(f\"Индекс точки с максимальной погрешностью: {max_error_index}\")\n",
     "    print(f\"Значения в точке с максимальной погрешностью:\")\n",
@@ -216,24 +187,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": null,
    "id": "89918171",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Максимальная относительная погрешность: 0.107830 (10.78%)\n",
-      "Индекс точки с максимальной погрешностью: 0\n",
-      "Значения в точке с максимальной погрешностью:\n",
-      "  cubic_real: 3.1950864987612366\n",
-      "  matrix_cubic: 2.85056\n",
-      "  Абсолютная разность: 0.344526\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "show_error(\"matrix_linear.txt\", \"linear_real_y_20.txt\")"
    ]