main_jupyter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from reinforced_concrete.sections import create_concrete_material, create_steel_material, Bars, ReinforcedConcreteSection, InternalForces\n",
    "\n",
    "from reinforced_concrete.sls import sls\n",
    "from reinforced_concrete.ULS import computeVero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "EXPORT_FOLDER = \"export/\"\n",
    "# TODO mettere il os.mkdir()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Single section:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Section name: sec1\n",
      "   CLS = C25/30\n",
      "     B = 400 mm\n",
      "     H = 350 mm\n",
      "     d = 310 mm\n",
      "    d1 = 40 mm\n",
      "    d2 = 40 mm\n",
      "    As = 10Ø16 =  2010 mm2 | B450C\n",
      "   As1 =  5Ø16 =  1005 mm2 | B450C\n",
      "   Med = 142400000.0 Nmm\n",
      "   Ned = -4400.0 N\n",
      "   Ved = 0.0 N\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cls  = create_concrete_material(\"EC2\",\"C25/30\") \n",
    "steel  = create_steel_material(\"NTC18\",\"B450C\")\n",
    "\n",
    "section_1 = ReinforcedConcreteSection(\n",
    "    b=600, \n",
    "    d=355, \n",
    "    d1=45, \n",
    "    d2=45, \n",
    "    concrete_material=cls, \n",
    "    As=Bars(n_bars=12, diameter=18, steel_material=steel), \n",
    "    As1=Bars(n_bars=9, diameter=18, steel_material=steel), \n",
    "    internal_forces=InternalForces(M=310*10**6, N=-150*10**3), \n",
    "    name=\"sec1\"\n",
    "    )\n",
    "\n",
    "section_1 = ReinforcedConcreteSection(\n",
    "    b=400, \n",
    "    d=310, \n",
    "    d1=40, \n",
    "    d2=40, \n",
    "    concrete_material=cls, \n",
    "    As=Bars(n_bars=10, diameter=16, steel_material=steel), \n",
    "    As1=Bars(n_bars=5, diameter=16, steel_material=steel), \n",
    "    internal_forces=InternalForces(M=142.4*10**6, N=-4.4*10**3), \n",
    "    name=\"sec1\"\n",
    "    )\n",
    "print(section_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SLS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'x': 136.56295282180042, 'sigma_c': 14.038687258610103, 'sigma_s': 267.44022599987994, 'sigma_s1': 148.9002398625023, '<sigma_cR': True, '<sigma_sR': True, '<sigma_sR1': True}\n",
      "Tenso-flessione\n"
     ]
    }
   ],
   "source": [
    "results_dict, logs = sls(section=section_1)\n",
    "print(results_dict)\n",
    "print(logs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ULS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'campo': '3A', 'es': 0.00924654547047127, 'es1': 0.00185528445542306, 'ec': 0.0035, 'xi': 0.274584200723884, 'psi': 0.8095238095238095, 'lamb': 0.41596638655462187, 'Nrd': -4399.99999999977, 'Mrd': 212923905.928571}\n"
     ]
    }
   ],
   "source": [
    "results_dict, logs = computeVero(section=section_1)\n",
    "print(results_dict)\n",
    "#print(logs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple sections ULS and SLS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "section_2 = ReinforcedConcreteSection(\n",
    "    b=400, \n",
    "    d=310, \n",
    "    d1=40, \n",
    "    d2=40, \n",
    "    concrete_material=cls, \n",
    "    As=Bars(n_bars=5, diameter=16, steel_material=steel), \n",
    "    As1=Bars(n_bars=5, diameter=12, steel_material=steel), \n",
    "    internal_forces=InternalForces(M=26407.2*10**6, N=22*10**3), \n",
    "    name=\"sec2\"\n",
    "    )\n",
    "section_3 = ReinforcedConcreteSection(\n",
    "    b=500, \n",
    "    d=710, \n",
    "    d1=40, \n",
    "    d2=40, \n",
    "    concrete_material=cls, \n",
    "    As=Bars(n_bars=7, diameter=16, steel_material=steel), \n",
    "    As1=Bars(n_bars=5, diameter=16, steel_material=steel), \n",
    "    internal_forces=InternalForces(M=207.2*10**6, N=22*10**3), \n",
    "    name=\"sec3\"\n",
    "    )\n",
    "list_of_sections = [section_1, section_2, section_3] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "      <th>H</th>\n",
       "      <th>d</th>\n",
       "      <th>d1</th>\n",
       "      <th>d2</th>\n",
       "      <th>As_str</th>\n",
       "      <th>As_area</th>\n",
       "      <th>As1_str</th>\n",
       "      <th>As1_area</th>\n",
       "      <th>Med</th>\n",
       "      <th>Ned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sec1</th>\n",
       "      <td>400</td>\n",
       "      <td>350</td>\n",
       "      <td>310</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>10Ø16</td>\n",
       "      <td>2009.60</td>\n",
       "      <td>5Ø16</td>\n",
       "      <td>1004.8</td>\n",
       "      <td>142.4</td>\n",
       "      <td>0.1424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sec2</th>\n",
       "      <td>400</td>\n",
       "      <td>350</td>\n",
       "      <td>310</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>5Ø16</td>\n",
       "      <td>1004.80</td>\n",
       "      <td>5Ø12</td>\n",
       "      <td>565.2</td>\n",
       "      <td>26407.2</td>\n",
       "      <td>26.4072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sec3</th>\n",
       "      <td>500</td>\n",
       "      <td>750</td>\n",
       "      <td>710</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>7Ø16</td>\n",
       "      <td>1406.72</td>\n",
       "      <td>5Ø16</td>\n",
       "      <td>1004.8</td>\n",
       "      <td>207.2</td>\n",
       "      <td>0.2072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        B    H    d  d1  d2 As_str  As_area As1_str  As1_area      Med  \\\n",
       "sec1  400  350  310  40  40  10Ø16  2009.60    5Ø16    1004.8    142.4   \n",
       "sec2  400  350  310  40  40   5Ø16  1004.80    5Ø12     565.2  26407.2   \n",
       "sec3  500  750  710  40  40   7Ø16  1406.72    5Ø16    1004.8    207.2   \n",
       "\n",
       "          Ned  \n",
       "sec1   0.1424  \n",
       "sec2  26.4072  \n",
       "sec3   0.2072  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_dict = [section.to_dict() for section in list_of_sections]\n",
    "row_names = [section.name for section in list_of_sections]\n",
    "tab_input = pd.DataFrame(input_dict, index=row_names)\n",
    "\n",
    "\n",
    "tab_input.drop([\"name\", \"CLS\", \"Steel\",\"Steel1\", \"Ved\"], axis=1, inplace=True) # Remove those columns\n",
    "tab_input[\"Med\"] = tab_input[\"Med\"].div(10**6) # Nmm -> kNm\n",
    "tab_input[\"Ned\"] = tab_input[\"Med\"].div(10**3) # N -> kN\n",
    "tab_input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ULS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'campo': '3A',\n",
       "  'es': 0.00924654547047127,\n",
       "  'es1': 0.00185528445542306,\n",
       "  'ec': 0.0035,\n",
       "  'xi': 0.274584200723884,\n",
       "  'psi': 0.8095238095238095,\n",
       "  'lamb': 0.41596638655462187,\n",
       "  'Nrd': -4399.99999999977,\n",
       "  'Mrd': 212923905.928571},\n",
       " {'campo': '2A',\n",
       "  'xi': 0.2148427370220405,\n",
       "  'es1': 0.0010929081727151162,\n",
       "  'ec': 0.002736301976080318,\n",
       "  'es': 0.01,\n",
       "  'psi': 0.756362173292858,\n",
       "  'lamb': 0.39780111548266606,\n",
       "  'Nrd': 21999.99999999936,\n",
       "  'Mrd': 115954755.13631497},\n",
       " {'campo': '2A',\n",
       "  'xi': 0.13395748978298877,\n",
       "  'es1': 0.0008962546375988511,\n",
       "  'ec': 0.001546777302530126,\n",
       "  'es': 0.01,\n",
       "  'psi': 0.5740119826298653,\n",
       "  'lamb': 0.3622782384730227,\n",
       "  'Nrd': 25379.08537669212,\n",
       "  'Mrd': 387953153.84976166}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_dicts_results_ULS = []\n",
    "for section in list_of_sections:\n",
    "    results_dict, logs = computeVero(section=section)\n",
    "    #print(logs)\n",
    "    list_of_dicts_results_ULS.append(results_dict)\n",
    "list_of_dicts_results_ULS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "      <th>H</th>\n",
       "      <th>d</th>\n",
       "      <th>d1</th>\n",
       "      <th>d2</th>\n",
       "      <th>As_str</th>\n",
       "      <th>As_area</th>\n",
       "      <th>As1_str</th>\n",
       "      <th>As1_area</th>\n",
       "      <th>Med</th>\n",
       "      <th>Ned</th>\n",
       "      <th>campo</th>\n",
       "      <th>es</th>\n",
       "      <th>es1</th>\n",
       "      <th>ec</th>\n",
       "      <th>xi</th>\n",
       "      <th>psi</th>\n",
       "      <th>lamb</th>\n",
       "      <th>Mrd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sec1</th>\n",
       "      <td>400</td>\n",
       "      <td>350</td>\n",
       "      <td>310</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>10Ø16</td>\n",
       "      <td>2009.60</td>\n",
       "      <td>5Ø16</td>\n",
       "      <td>1004.8</td>\n",
       "      <td>142.4</td>\n",
       "      <td>0.1424</td>\n",
       "      <td>3A</td>\n",
       "      <td>0.00924654547047127</td>\n",
       "      <td>0.00185528445542306</td>\n",
       "      <td>0.003500</td>\n",
       "      <td>0.274584200723884</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.415966</td>\n",
       "      <td>212.923905928571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sec2</th>\n",
       "      <td>400</td>\n",
       "      <td>350</td>\n",
       "      <td>310</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>5Ø16</td>\n",
       "      <td>1004.80</td>\n",
       "      <td>5Ø12</td>\n",
       "      <td>565.2</td>\n",
       "      <td>26407.2</td>\n",
       "      <td>26.4072</td>\n",
       "      <td>2A</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.001093</td>\n",
       "      <td>0.002736</td>\n",
       "      <td>0.214843</td>\n",
       "      <td>0.756362</td>\n",
       "      <td>0.397801</td>\n",
       "      <td>115.954755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sec3</th>\n",
       "      <td>500</td>\n",
       "      <td>750</td>\n",
       "      <td>710</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>7Ø16</td>\n",
       "      <td>1406.72</td>\n",
       "      <td>5Ø16</td>\n",
       "      <td>1004.8</td>\n",
       "      <td>207.2</td>\n",
       "      <td>0.2072</td>\n",
       "      <td>2A</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000896</td>\n",
       "      <td>0.001547</td>\n",
       "      <td>0.133957</td>\n",
       "      <td>0.574012</td>\n",
       "      <td>0.362278</td>\n",
       "      <td>387.953154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        B    H    d  d1  d2 As_str  As_area As1_str  As1_area      Med  \\\n",
       "sec1  400  350  310  40  40  10Ø16  2009.60    5Ø16    1004.8    142.4   \n",
       "sec2  400  350  310  40  40   5Ø16  1004.80    5Ø12     565.2  26407.2   \n",
       "sec3  500  750  710  40  40   7Ø16  1406.72    5Ø16    1004.8    207.2   \n",
       "\n",
       "          Ned campo                   es                  es1        ec  \\\n",
       "sec1   0.1424    3A  0.00924654547047127  0.00185528445542306  0.003500   \n",
       "sec2  26.4072    2A                 0.01             0.001093  0.002736   \n",
       "sec3   0.2072    2A                 0.01             0.000896  0.001547   \n",
       "\n",
       "                     xi       psi      lamb               Mrd  \n",
       "sec1  0.274584200723884  0.809524  0.415966  212.923905928571  \n",
       "sec2           0.214843  0.756362  0.397801        115.954755  \n",
       "sec3           0.133957  0.574012  0.362278        387.953154  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uls_tab_check = pd.DataFrame(list_of_dicts_results_ULS, index=row_names)\n",
    "\n",
    "uls_tab_tot = tab_input.join(uls_tab_check) \n",
    "uls_tab_tot.drop([\"Nrd\"], axis=1, inplace=True)\n",
    "uls_tab_tot[\"Mrd\"] = uls_tab_tot[\"Mrd\"].div(10**6) # Nmm -> kNm\n",
    "uls_tab_tot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tenso-flessione\n",
      "Presso-flessione\n",
      "Presso-flessione\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'x': 136.56295282180042,\n",
       "  'sigma_c': 14.038687258610103,\n",
       "  'sigma_s': 267.44022599987994,\n",
       "  'sigma_s1': 148.9002398625023,\n",
       "  '<sigma_cR': True,\n",
       "  '<sigma_sR': True,\n",
       "  '<sigma_sR1': True},\n",
       " {'x': 110.02405095513399,\n",
       "  'sigma_c': 3534.8694716920504,\n",
       "  'sigma_s': 96372.86637077192,\n",
       "  'sigma_s1': 33746.15066297396,\n",
       "  '<sigma_cR': False,\n",
       "  '<sigma_sR': False,\n",
       "  '<sigma_sR1': False},\n",
       " {'x': 193.52300660308478,\n",
       "  'sigma_c': 5.468088890542552,\n",
       "  'sigma_s': 218.8997183890492,\n",
       "  'sigma_s1': 65.06803466815589,\n",
       "  '<sigma_cR': True,\n",
       "  '<sigma_sR': True,\n",
       "  '<sigma_sR1': True}]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_dicts_results_SLS = []\n",
    "for section in list_of_sections:\n",
    "    results_dict, logs = sls(section=section)\n",
    "    print(logs)\n",
    "    list_of_dicts_results_SLS.append(results_dict)\n",
    "list_of_dicts_results_SLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "      <th>H</th>\n",
       "      <th>d</th>\n",
       "      <th>d1</th>\n",
       "      <th>d2</th>\n",
       "      <th>As_str</th>\n",
       "      <th>As_area</th>\n",
       "      <th>As1_str</th>\n",
       "      <th>As1_area</th>\n",
       "      <th>Med</th>\n",
       "      <th>Ned</th>\n",
       "      <th>x</th>\n",
       "      <th>sigma_c</th>\n",
       "      <th>sigma_s</th>\n",
       "      <th>sigma_s1</th>\n",
       "      <th>&lt;sigma_cR</th>\n",
       "      <th>&lt;sigma_sR</th>\n",
       "      <th>&lt;sigma_sR1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sec1</th>\n",
       "      <td>400</td>\n",
       "      <td>350</td>\n",
       "      <td>310</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>10Ø16</td>\n",
       "      <td>2009.60</td>\n",
       "      <td>5Ø16</td>\n",
       "      <td>1004.8</td>\n",
       "      <td>142.4</td>\n",
       "      <td>0.1424</td>\n",
       "      <td>136.562953</td>\n",
       "      <td>14.038687</td>\n",
       "      <td>267.440226</td>\n",
       "      <td>148.900240</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sec2</th>\n",
       "      <td>400</td>\n",
       "      <td>350</td>\n",
       "      <td>310</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>5Ø16</td>\n",
       "      <td>1004.80</td>\n",
       "      <td>5Ø12</td>\n",
       "      <td>565.2</td>\n",
       "      <td>26407.2</td>\n",
       "      <td>26.4072</td>\n",
       "      <td>110.024051</td>\n",
       "      <td>3534.869472</td>\n",
       "      <td>96372.866371</td>\n",
       "      <td>33746.150663</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sec3</th>\n",
       "      <td>500</td>\n",
       "      <td>750</td>\n",
       "      <td>710</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>7Ø16</td>\n",
       "      <td>1406.72</td>\n",
       "      <td>5Ø16</td>\n",
       "      <td>1004.8</td>\n",
       "      <td>207.2</td>\n",
       "      <td>0.2072</td>\n",
       "      <td>193.523007</td>\n",
       "      <td>5.468089</td>\n",
       "      <td>218.899718</td>\n",
       "      <td>65.068035</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        B    H    d  d1  d2 As_str  As_area As1_str  As1_area      Med  \\\n",
       "sec1  400  350  310  40  40  10Ø16  2009.60    5Ø16    1004.8    142.4   \n",
       "sec2  400  350  310  40  40   5Ø16  1004.80    5Ø12     565.2  26407.2   \n",
       "sec3  500  750  710  40  40   7Ø16  1406.72    5Ø16    1004.8    207.2   \n",
       "\n",
       "          Ned           x      sigma_c       sigma_s      sigma_s1 <sigma_cR  \\\n",
       "sec1   0.1424  136.562953    14.038687    267.440226    148.900240      True   \n",
       "sec2  26.4072  110.024051  3534.869472  96372.866371  33746.150663     False   \n",
       "sec3   0.2072  193.523007     5.468089    218.899718     65.068035      True   \n",
       "\n",
       "     <sigma_sR <sigma_sR1  \n",
       "sec1      True       True  \n",
       "sec2     False      False  \n",
       "sec3      True       True  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sls_tab_check = pd.DataFrame(list_of_dicts_results_SLS, index=row_names)\n",
    "sls_tab_tot = tab_input.join(sls_tab_check) #l'altro metodo è merge\n",
    "sls_tab_tot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_cc5c1\">\n",
       "  <caption>ULS</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_cc5c1_level0_col0\" class=\"col_heading level0 col0\" >B</th>\n",
       "      <th id=\"T_cc5c1_level0_col1\" class=\"col_heading level0 col1\" >H</th>\n",
       "      <th id=\"T_cc5c1_level0_col2\" class=\"col_heading level0 col2\" >d</th>\n",
       "      <th id=\"T_cc5c1_level0_col3\" class=\"col_heading level0 col3\" >d1</th>\n",
       "      <th id=\"T_cc5c1_level0_col4\" class=\"col_heading level0 col4\" >d2</th>\n",
       "      <th id=\"T_cc5c1_level0_col5\" class=\"col_heading level0 col5\" >As_str</th>\n",
       "      <th id=\"T_cc5c1_level0_col6\" class=\"col_heading level0 col6\" >As_area</th>\n",
       "      <th id=\"T_cc5c1_level0_col7\" class=\"col_heading level0 col7\" >As1_str</th>\n",
       "      <th id=\"T_cc5c1_level0_col8\" class=\"col_heading level0 col8\" >As1_area</th>\n",
       "      <th id=\"T_cc5c1_level0_col9\" class=\"col_heading level0 col9\" >Med</th>\n",
       "      <th id=\"T_cc5c1_level0_col10\" class=\"col_heading level0 col10\" >Ned</th>\n",
       "      <th id=\"T_cc5c1_level0_col11\" class=\"col_heading level0 col11\" >campo</th>\n",
       "      <th id=\"T_cc5c1_level0_col12\" class=\"col_heading level0 col12\" >es</th>\n",
       "      <th id=\"T_cc5c1_level0_col13\" class=\"col_heading level0 col13\" >es1</th>\n",
       "      <th id=\"T_cc5c1_level0_col14\" class=\"col_heading level0 col14\" >ec</th>\n",
       "      <th id=\"T_cc5c1_level0_col15\" class=\"col_heading level0 col15\" >xi</th>\n",
       "      <th id=\"T_cc5c1_level0_col16\" class=\"col_heading level0 col16\" >psi</th>\n",
       "      <th id=\"T_cc5c1_level0_col17\" class=\"col_heading level0 col17\" >lamb</th>\n",
       "      <th id=\"T_cc5c1_level0_col18\" class=\"col_heading level0 col18\" >Mrd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_cc5c1_level0_row0\" class=\"row_heading level0 row0\" >sec1</th>\n",
       "      <td id=\"T_cc5c1_row0_col0\" class=\"data row0 col0\" >400</td>\n",
       "      <td id=\"T_cc5c1_row0_col1\" class=\"data row0 col1\" >350</td>\n",
       "      <td id=\"T_cc5c1_row0_col2\" class=\"data row0 col2\" >310</td>\n",
       "      <td id=\"T_cc5c1_row0_col3\" class=\"data row0 col3\" >40</td>\n",
       "      <td id=\"T_cc5c1_row0_col4\" class=\"data row0 col4\" >40</td>\n",
       "      <td id=\"T_cc5c1_row0_col5\" class=\"data row0 col5\" >10Ø16</td>\n",
       "      <td id=\"T_cc5c1_row0_col6\" class=\"data row0 col6\" >2010</td>\n",
       "      <td id=\"T_cc5c1_row0_col7\" class=\"data row0 col7\" >5Ø16</td>\n",
       "      <td id=\"T_cc5c1_row0_col8\" class=\"data row0 col8\" >1005</td>\n",
       "      <td id=\"T_cc5c1_row0_col9\" class=\"data row0 col9\" >142.400</td>\n",
       "      <td id=\"T_cc5c1_row0_col10\" class=\"data row0 col10\" >0.142</td>\n",
       "      <td id=\"T_cc5c1_row0_col11\" class=\"data row0 col11\" >3A</td>\n",
       "      <td id=\"T_cc5c1_row0_col12\" class=\"data row0 col12\" >0.0092</td>\n",
       "      <td id=\"T_cc5c1_row0_col13\" class=\"data row0 col13\" >0.0019</td>\n",
       "      <td id=\"T_cc5c1_row0_col14\" class=\"data row0 col14\" >0.0035</td>\n",
       "      <td id=\"T_cc5c1_row0_col15\" class=\"data row0 col15\" >0.2746</td>\n",
       "      <td id=\"T_cc5c1_row0_col16\" class=\"data row0 col16\" >0.8095</td>\n",
       "      <td id=\"T_cc5c1_row0_col17\" class=\"data row0 col17\" >0.4160</td>\n",
       "      <td id=\"T_cc5c1_row0_col18\" class=\"data row0 col18\" >212.924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc5c1_level0_row1\" class=\"row_heading level0 row1\" >sec2</th>\n",
       "      <td id=\"T_cc5c1_row1_col0\" class=\"data row1 col0\" >400</td>\n",
       "      <td id=\"T_cc5c1_row1_col1\" class=\"data row1 col1\" >350</td>\n",
       "      <td id=\"T_cc5c1_row1_col2\" class=\"data row1 col2\" >310</td>\n",
       "      <td id=\"T_cc5c1_row1_col3\" class=\"data row1 col3\" >40</td>\n",
       "      <td id=\"T_cc5c1_row1_col4\" class=\"data row1 col4\" >40</td>\n",
       "      <td id=\"T_cc5c1_row1_col5\" class=\"data row1 col5\" >5Ø16</td>\n",
       "      <td id=\"T_cc5c1_row1_col6\" class=\"data row1 col6\" >1005</td>\n",
       "      <td id=\"T_cc5c1_row1_col7\" class=\"data row1 col7\" >5Ø12</td>\n",
       "      <td id=\"T_cc5c1_row1_col8\" class=\"data row1 col8\" >565</td>\n",
       "      <td id=\"T_cc5c1_row1_col9\" class=\"data row1 col9\" >26407.200</td>\n",
       "      <td id=\"T_cc5c1_row1_col10\" class=\"data row1 col10\" >26.407</td>\n",
       "      <td id=\"T_cc5c1_row1_col11\" class=\"data row1 col11\" >2A</td>\n",
       "      <td id=\"T_cc5c1_row1_col12\" class=\"data row1 col12\" >0.0100</td>\n",
       "      <td id=\"T_cc5c1_row1_col13\" class=\"data row1 col13\" >0.0011</td>\n",
       "      <td id=\"T_cc5c1_row1_col14\" class=\"data row1 col14\" >0.0027</td>\n",
       "      <td id=\"T_cc5c1_row1_col15\" class=\"data row1 col15\" >0.2148</td>\n",
       "      <td id=\"T_cc5c1_row1_col16\" class=\"data row1 col16\" >0.7564</td>\n",
       "      <td id=\"T_cc5c1_row1_col17\" class=\"data row1 col17\" >0.3978</td>\n",
       "      <td id=\"T_cc5c1_row1_col18\" class=\"data row1 col18\" >115.955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc5c1_level0_row2\" class=\"row_heading level0 row2\" >sec3</th>\n",
       "      <td id=\"T_cc5c1_row2_col0\" class=\"data row2 col0\" >500</td>\n",
       "      <td id=\"T_cc5c1_row2_col1\" class=\"data row2 col1\" >750</td>\n",
       "      <td id=\"T_cc5c1_row2_col2\" class=\"data row2 col2\" >710</td>\n",
       "      <td id=\"T_cc5c1_row2_col3\" class=\"data row2 col3\" >40</td>\n",
       "      <td id=\"T_cc5c1_row2_col4\" class=\"data row2 col4\" >40</td>\n",
       "      <td id=\"T_cc5c1_row2_col5\" class=\"data row2 col5\" >7Ø16</td>\n",
       "      <td id=\"T_cc5c1_row2_col6\" class=\"data row2 col6\" >1407</td>\n",
       "      <td id=\"T_cc5c1_row2_col7\" class=\"data row2 col7\" >5Ø16</td>\n",
       "      <td id=\"T_cc5c1_row2_col8\" class=\"data row2 col8\" >1005</td>\n",
       "      <td id=\"T_cc5c1_row2_col9\" class=\"data row2 col9\" >207.200</td>\n",
       "      <td id=\"T_cc5c1_row2_col10\" class=\"data row2 col10\" >0.207</td>\n",
       "      <td id=\"T_cc5c1_row2_col11\" class=\"data row2 col11\" >2A</td>\n",
       "      <td id=\"T_cc5c1_row2_col12\" class=\"data row2 col12\" >0.0100</td>\n",
       "      <td id=\"T_cc5c1_row2_col13\" class=\"data row2 col13\" >0.0009</td>\n",
       "      <td id=\"T_cc5c1_row2_col14\" class=\"data row2 col14\" >0.0015</td>\n",
       "      <td id=\"T_cc5c1_row2_col15\" class=\"data row2 col15\" >0.1340</td>\n",
       "      <td id=\"T_cc5c1_row2_col16\" class=\"data row2 col16\" >0.5740</td>\n",
       "      <td id=\"T_cc5c1_row2_col17\" class=\"data row2 col17\" >0.3623</td>\n",
       "      <td id=\"T_cc5c1_row2_col18\" class=\"data row2 col18\" >387.953</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x20a6437dd30>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#STOP\n",
    "s = uls_tab_tot.style\n",
    "s.clear()\n",
    "s.caption = \"ULS\"\n",
    "s.format('{:.0f}', subset=[\"As_area\",\"As1_area\"])\n",
    "s.format(\"{:.3f}\", subset=[\"Med\",\"Ned\", \"Mrd\"])\n",
    "s.format(\"{:.4f}\", subset=[\"es\",\"es1\",\"ec\", \"xi\",\"psi\", \"lamb\"])\n",
    "\n",
    "#with open(EXPORT_FOLDER+\"prova.tex\", 'w') as file:\n",
    " #   file.write(s.to_latex(position='H', hrules=True, siunitx=True))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_fcaf5\">\n",
       "  <caption>SLS</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_fcaf5_level0_col0\" class=\"col_heading level0 col0\" >B</th>\n",
       "      <th id=\"T_fcaf5_level0_col1\" class=\"col_heading level0 col1\" >H</th>\n",
       "      <th id=\"T_fcaf5_level0_col2\" class=\"col_heading level0 col2\" >d</th>\n",
       "      <th id=\"T_fcaf5_level0_col3\" class=\"col_heading level0 col3\" >d1</th>\n",
       "      <th id=\"T_fcaf5_level0_col4\" class=\"col_heading level0 col4\" >d2</th>\n",
       "      <th id=\"T_fcaf5_level0_col5\" class=\"col_heading level0 col5\" >As_str</th>\n",
       "      <th id=\"T_fcaf5_level0_col6\" class=\"col_heading level0 col6\" >As_area</th>\n",
       "      <th id=\"T_fcaf5_level0_col7\" class=\"col_heading level0 col7\" >As1_str</th>\n",
       "      <th id=\"T_fcaf5_level0_col8\" class=\"col_heading level0 col8\" >As1_area</th>\n",
       "      <th id=\"T_fcaf5_level0_col9\" class=\"col_heading level0 col9\" >Med</th>\n",
       "      <th id=\"T_fcaf5_level0_col10\" class=\"col_heading level0 col10\" >Ned</th>\n",
       "      <th id=\"T_fcaf5_level0_col11\" class=\"col_heading level0 col11\" >x</th>\n",
       "      <th id=\"T_fcaf5_level0_col12\" class=\"col_heading level0 col12\" >sigma_c</th>\n",
       "      <th id=\"T_fcaf5_level0_col13\" class=\"col_heading level0 col13\" >sigma_s</th>\n",
       "      <th id=\"T_fcaf5_level0_col14\" class=\"col_heading level0 col14\" >sigma_s1</th>\n",
       "      <th id=\"T_fcaf5_level0_col15\" class=\"col_heading level0 col15\" ><sigma_cR</th>\n",
       "      <th id=\"T_fcaf5_level0_col16\" class=\"col_heading level0 col16\" ><sigma_sR</th>\n",
       "      <th id=\"T_fcaf5_level0_col17\" class=\"col_heading level0 col17\" ><sigma_sR1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_fcaf5_level0_row0\" class=\"row_heading level0 row0\" >sec1</th>\n",
       "      <td id=\"T_fcaf5_row0_col0\" class=\"data row0 col0\" >400</td>\n",
       "      <td id=\"T_fcaf5_row0_col1\" class=\"data row0 col1\" >350</td>\n",
       "      <td id=\"T_fcaf5_row0_col2\" class=\"data row0 col2\" >310</td>\n",
       "      <td id=\"T_fcaf5_row0_col3\" class=\"data row0 col3\" >40</td>\n",
       "      <td id=\"T_fcaf5_row0_col4\" class=\"data row0 col4\" >40</td>\n",
       "      <td id=\"T_fcaf5_row0_col5\" class=\"data row0 col5\" >10Ø16</td>\n",
       "      <td id=\"T_fcaf5_row0_col6\" class=\"data row0 col6\" >2010</td>\n",
       "      <td id=\"T_fcaf5_row0_col7\" class=\"data row0 col7\" >5Ø16</td>\n",
       "      <td id=\"T_fcaf5_row0_col8\" class=\"data row0 col8\" >1005</td>\n",
       "      <td id=\"T_fcaf5_row0_col9\" class=\"data row0 col9\" >142.400</td>\n",
       "      <td id=\"T_fcaf5_row0_col10\" class=\"data row0 col10\" >0.142</td>\n",
       "      <td id=\"T_fcaf5_row0_col11\" class=\"data row0 col11\" >136.56</td>\n",
       "      <td id=\"T_fcaf5_row0_col12\" class=\"data row0 col12\" >14.04</td>\n",
       "      <td id=\"T_fcaf5_row0_col13\" class=\"data row0 col13\" >267.44</td>\n",
       "      <td id=\"T_fcaf5_row0_col14\" class=\"data row0 col14\" >148.90</td>\n",
       "      <td id=\"T_fcaf5_row0_col15\" class=\"data row0 col15\" >True</td>\n",
       "      <td id=\"T_fcaf5_row0_col16\" class=\"data row0 col16\" >True</td>\n",
       "      <td id=\"T_fcaf5_row0_col17\" class=\"data row0 col17\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fcaf5_level0_row1\" class=\"row_heading level0 row1\" >sec2</th>\n",
       "      <td id=\"T_fcaf5_row1_col0\" class=\"data row1 col0\" >400</td>\n",
       "      <td id=\"T_fcaf5_row1_col1\" class=\"data row1 col1\" >350</td>\n",
       "      <td id=\"T_fcaf5_row1_col2\" class=\"data row1 col2\" >310</td>\n",
       "      <td id=\"T_fcaf5_row1_col3\" class=\"data row1 col3\" >40</td>\n",
       "      <td id=\"T_fcaf5_row1_col4\" class=\"data row1 col4\" >40</td>\n",
       "      <td id=\"T_fcaf5_row1_col5\" class=\"data row1 col5\" >5Ø16</td>\n",
       "      <td id=\"T_fcaf5_row1_col6\" class=\"data row1 col6\" >1005</td>\n",
       "      <td id=\"T_fcaf5_row1_col7\" class=\"data row1 col7\" >5Ø12</td>\n",
       "      <td id=\"T_fcaf5_row1_col8\" class=\"data row1 col8\" >565</td>\n",
       "      <td id=\"T_fcaf5_row1_col9\" class=\"data row1 col9\" >26407.200</td>\n",
       "      <td id=\"T_fcaf5_row1_col10\" class=\"data row1 col10\" >26.407</td>\n",
       "      <td id=\"T_fcaf5_row1_col11\" class=\"data row1 col11\" >110.02</td>\n",
       "      <td id=\"T_fcaf5_row1_col12\" class=\"data row1 col12\" >3534.87</td>\n",
       "      <td id=\"T_fcaf5_row1_col13\" class=\"data row1 col13\" >96372.87</td>\n",
       "      <td id=\"T_fcaf5_row1_col14\" class=\"data row1 col14\" >33746.15</td>\n",
       "      <td id=\"T_fcaf5_row1_col15\" class=\"data row1 col15\" >False</td>\n",
       "      <td id=\"T_fcaf5_row1_col16\" class=\"data row1 col16\" >False</td>\n",
       "      <td id=\"T_fcaf5_row1_col17\" class=\"data row1 col17\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fcaf5_level0_row2\" class=\"row_heading level0 row2\" >sec3</th>\n",
       "      <td id=\"T_fcaf5_row2_col0\" class=\"data row2 col0\" >500</td>\n",
       "      <td id=\"T_fcaf5_row2_col1\" class=\"data row2 col1\" >750</td>\n",
       "      <td id=\"T_fcaf5_row2_col2\" class=\"data row2 col2\" >710</td>\n",
       "      <td id=\"T_fcaf5_row2_col3\" class=\"data row2 col3\" >40</td>\n",
       "      <td id=\"T_fcaf5_row2_col4\" class=\"data row2 col4\" >40</td>\n",
       "      <td id=\"T_fcaf5_row2_col5\" class=\"data row2 col5\" >7Ø16</td>\n",
       "      <td id=\"T_fcaf5_row2_col6\" class=\"data row2 col6\" >1407</td>\n",
       "      <td id=\"T_fcaf5_row2_col7\" class=\"data row2 col7\" >5Ø16</td>\n",
       "      <td id=\"T_fcaf5_row2_col8\" class=\"data row2 col8\" >1005</td>\n",
       "      <td id=\"T_fcaf5_row2_col9\" class=\"data row2 col9\" >207.200</td>\n",
       "      <td id=\"T_fcaf5_row2_col10\" class=\"data row2 col10\" >0.207</td>\n",
       "      <td id=\"T_fcaf5_row2_col11\" class=\"data row2 col11\" >193.52</td>\n",
       "      <td id=\"T_fcaf5_row2_col12\" class=\"data row2 col12\" >5.47</td>\n",
       "      <td id=\"T_fcaf5_row2_col13\" class=\"data row2 col13\" >218.90</td>\n",
       "      <td id=\"T_fcaf5_row2_col14\" class=\"data row2 col14\" >65.07</td>\n",
       "      <td id=\"T_fcaf5_row2_col15\" class=\"data row2 col15\" >True</td>\n",
       "      <td id=\"T_fcaf5_row2_col16\" class=\"data row2 col16\" >True</td>\n",
       "      <td id=\"T_fcaf5_row2_col17\" class=\"data row2 col17\" >True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x20a652d0670>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#STOP\n",
    "s = sls_tab_tot.style\n",
    "s.clear()\n",
    "s.caption = \"SLS\"\n",
    "s.format('{:.0f}', subset=[\"As_area\",\"As1_area\"])\n",
    "s.format('{:.2f}', subset=[\"x\",'sigma_c','sigma_s','sigma_s1'])\n",
    "s.format(\"{:.3f}\", subset=[\"Med\",\"Ned\"])\n",
    "\n",
    "#with open(EXPORT_FOLDER+\"prova.tex\", 'w') as file:\n",
    " #   file.write(s.to_latex(position='H', hrules=True, siunitx=True))\n",
    "s\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\\\begin{table}\\n\\\\caption{SLS}\\n\\\\begin{tabular}{lrrrrrlrlrrrrrrrlll}\\n & B & H & d & d1 & d2 & As_str & As_area & As1_str & As1_area & Med & Ned & x & sigma_c & sigma_s & sigma_s1 & <sigma_cR & <sigma_sR & <sigma_sR1 \\\\\\\\\\nsec1 & 400 & 350 & 310 & 40 & 40 & 10Ø16 & 2010 & 5Ø16 & 1005 & 142.400 & 0.142 & 136.56 & 14.04 & 267.44 & 148.90 & True & True & True \\\\\\\\\\nsec2 & 400 & 350 & 310 & 40 & 40 & 5Ø16 & 1005 & 5Ø12 & 565 & 26407.200 & 26.407 & 110.02 & 3534.87 & 96372.87 & 33746.15 & False & False & False \\\\\\\\\\nsec3 & 500 & 750 & 710 & 40 & 40 & 7Ø16 & 1407 & 5Ø16 & 1005 & 207.200 & 0.207 & 193.52 & 5.47 & 218.90 & 65.07 & True & True & True \\\\\\\\\\n\\\\end{tabular}\\n\\\\end{table}\\n'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.to_latex()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "e9dfbbb36e3b3679682f891148ee1981976897dd5367a65a9c1bc658de856a84"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 ('st')",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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