hsugawa8651/20171122-dataframe-loglog.ipynb

## 20171122-dataframe-loglog.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mPackage DataFrames is already installed\n",
      "\u001b[39m\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mMETADATA is out-of-date — you may not have the latest version of DataFrames\n",
      "\u001b[39m\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mUse `Pkg.update()` to get the latest versions of your packages\n",
      "\u001b[39m"
     ]
    }
   ],
   "source": [
    "Pkg.add(\"DataFrames\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mUpdating METADATA...\n",
      "\u001b[39m\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mNo packages to install, update or remove\n",
      "\u001b[39m\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mComputing changes...\n",
      "\u001b[39m"
     ]
    }
   ],
   "source": [
    "Pkg.update()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "using DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd(\"/Users/hs/Documents/__Class/17w-nano-dev/conductivity\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>element</th><th>n</th><th>rH</th><th>mu</th><th>conductivity</th></tr></thead><tbody><tr><th>1</th><td>Ag</td><td>5.9e22</td><td>-9.0</td><td>57</td><td>534000.0</td></tr><tr><th>2</th><td>Al</td><td>1.8e23</td><td>-3.5</td><td>13</td><td>376000.0</td></tr><tr><th>3</th><td>Au</td><td>5.9e22</td><td>-7.2</td><td>31</td><td>293000.0</td></tr><tr><th>4</th><td>Cu</td><td>8.4e22</td><td>-5.5</td><td>32</td><td>433000.0</td></tr><tr><th>5</th><td>Ga</td><td>1.5e23</td><td>6.3</td><td>4</td><td>88200.0</td></tr><tr><th>6</th><td>In</td><td>1.1e23</td><td>-2.4</td><td>3</td><td>53400.0</td></tr><tr><th>7</th><td>Mg</td><td>8.6e22</td><td>-9.4</td><td>22</td><td>303000.0</td></tr><tr><th>8</th><td>Na</td><td>2.6e22</td><td>-25.0</td><td>53</td><td>217000.0</td></tr></tbody></table>"
      ],
      "text/plain": [
       "8×5 DataFrames.DataFrame\n",
       "│ Row │ element │ n      │ rH    │ mu │ conductivity │\n",
       "├─────┼─────────┼────────┼───────┼────┼──────────────┤\n",
       "│ 1   │ \"Ag\"    │ 5.9e22 │ -9.0  │ 57 │ 534000.0     │\n",
       "│ 2   │ \"Al\"    │ 1.8e23 │ -3.5  │ 13 │ 376000.0     │\n",
       "│ 3   │ \"Au\"    │ 5.9e22 │ -7.2  │ 31 │ 293000.0     │\n",
       "│ 4   │ \"Cu\"    │ 8.4e22 │ -5.5  │ 32 │ 433000.0     │\n",
       "│ 5   │ \"Ga\"    │ 1.5e23 │ 6.3   │ 4  │ 88200.0      │\n",
       "│ 6   │ \"In\"    │ 1.1e23 │ -2.4  │ 3  │ 53400.0      │\n",
       "│ 7   │ \"Mg\"    │ 8.6e22 │ -9.4  │ 22 │ 303000.0     │\n",
       "│ 8   │ \"Na\"    │ 2.6e22 │ -25.0 │ 53 │ 217000.0     │"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metal=readtable(\"metal.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5-element Array{Symbol,1}:\n",
       " :element     \n",
       " :n           \n",
       " :rH          \n",
       " :mu          \n",
       " :conductivity"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names(metal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8-element DataArrays.DataArray{String,1}:\n",
       " \"Ag\"\n",
       " \"Al\"\n",
       " \"Au\"\n",
       " \"Cu\"\n",
       " \"Ga\"\n",
       " \"In\"\n",
       " \"Mg\"\n",
       " \"Na\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metal[:element]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8-element DataArrays.DataArray{Float64,1}:\n",
       " 5.9e22\n",
       " 1.8e23\n",
       " 5.9e22\n",
       " 8.4e22\n",
       " 1.5e23\n",
       " 1.1e23\n",
       " 8.6e22\n",
       " 2.6e22"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metal[:n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8-element DataArrays.DataArray{Int64,1}:\n",
       " 57\n",
       " 13\n",
       " 31\n",
       " 32\n",
       "  4\n",
       "  3\n",
       " 22\n",
       " 53"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metal[:mu]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>element</th><th>n</th><th>muE</th><th>muH</th></tr></thead><tbody><tr><th>1</th><td>Ge</td><td>2.3e13</td><td>3900</td><td>1900</td></tr><tr><th>2</th><td>Si</td><td>1.0e10</td><td>1350</td><td>450</td></tr><tr><th>3</th><td>GaAs</td><td>2.1e6</td><td>8500</td><td>400</td></tr></tbody></table>"
      ],
      "text/plain": [
       "3×4 DataFrames.DataFrame\n",
       "│ Row │ element │ n      │ muE  │ muH  │\n",
       "├─────┼─────────┼────────┼──────┼──────┤\n",
       "│ 1   │ \"Ge\"    │ 2.3e13 │ 3900 │ 1900 │\n",
       "│ 2   │ \"Si\"    │ 1.0e10 │ 1350 │ 450  │\n",
       "│ 3   │ \"GaAs\"  │ 2.1e6  │ 8500 │ 400  │"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intr=readtable(\"intr.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>n</th><th>muE</th></tr></thead><tbody><tr><th>1</th><td>9.66e14</td><td>1506.79</td></tr><tr><th>2</th><td>2.62e15</td><td>1391.02</td></tr><tr><th>3</th><td>1.59e16</td><td>1161.29</td></tr><tr><th>4</th><td>7.74e16</td><td>825.904</td></tr><tr><th>5</th><td>2.31e17</td><td>556.113</td></tr><tr><th>6</th><td>6.43e17</td><td>336.639</td></tr><tr><th>7</th><td>1.84e18</td><td>190.035</td></tr><tr><th>8</th><td>5.34e18</td><td>124.244</td></tr><tr><th>9</th><td>1.82e19</td><td>96.1191</td></tr><tr><th>10</th><td>5.16e19</td><td>88.4732</td></tr><tr><th>11</th><td>9.41e19</td><td>88.8089</td></tr></tbody></table>"
      ],
      "text/plain": [
       "11×2 DataFrames.DataFrame\n",
       "│ Row │ n       │ muE     │\n",
       "├─────┼─────────┼─────────┤\n",
       "│ 1   │ 9.66e14 │ 1506.79 │\n",
       "│ 2   │ 2.62e15 │ 1391.02 │\n",
       "│ 3   │ 1.59e16 │ 1161.29 │\n",
       "│ 4   │ 7.74e16 │ 825.904 │\n",
       "│ 5   │ 2.31e17 │ 556.113 │\n",
       "│ 6   │ 6.43e17 │ 336.639 │\n",
       "│ 7   │ 1.84e18 │ 190.035 │\n",
       "│ 8   │ 5.34e18 │ 124.244 │\n",
       "│ 9   │ 1.82e19 │ 96.1191 │\n",
       "│ 10  │ 5.16e19 │ 88.4732 │\n",
       "│ 11  │ 9.41e19 │ 88.8089 │"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "siN=readtable(\"siN.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>p</th><th>muH</th></tr></thead><tbody><tr><th>1</th><td>9.67e14</td><td>496.393</td></tr><tr><th>2</th><td>1.2e15</td><td>496.393</td></tr><tr><th>3</th><td>1.75e15</td><td>492.648</td></tr><tr><th>4</th><td>2.28e15</td><td>479.76</td></tr><tr><th>5</th><td>2.36e15</td><td>481.58</td></tr><tr><th>6</th><td>4.11e15</td><td>465.444</td></tr><tr><th>7</th><td>6.66e15</td><td>426.621</td></tr><tr><th>8</th><td>8.07e16</td><td>283.418</td></tr><tr><th>9</th><td>7.96e17</td><td>145.54</td></tr><tr><th>10</th><td>2.56e18</td><td>100.799</td></tr><tr><th>11</th><td>9.08e18</td><td>82.1583</td></tr></tbody></table>"
      ],
      "text/plain": [
       "11×2 DataFrames.DataFrame\n",
       "│ Row │ p       │ muH     │\n",
       "├─────┼─────────┼─────────┤\n",
       "│ 1   │ 9.67e14 │ 496.393 │\n",
       "│ 2   │ 1.2e15  │ 496.393 │\n",
       "│ 3   │ 1.75e15 │ 492.648 │\n",
       "│ 4   │ 2.28e15 │ 479.76  │\n",
       "│ 5   │ 2.36e15 │ 481.58  │\n",
       "│ 6   │ 4.11e15 │ 465.444 │\n",
       "│ 7   │ 6.66e15 │ 426.621 │\n",
       "│ 8   │ 8.07e16 │ 283.418 │\n",
       "│ 9   │ 7.96e17 │ 145.54  │\n",
       "│ 10  │ 2.56e18 │ 100.799 │\n",
       "│ 11  │ 9.08e18 │ 82.1583 │"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "siP=readtable(\"siP.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "using PyPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KVeSutD0KAPhNiO0BAPyyNVu/0tysHao1UpAjZUxIVtrg7rbHAoBG44wI0MwVuSs9ESJJtUZ6MGsnZ0YAtAqECNDMFZRWeCLkpBpjdKD0qJ2BAMCPCBGgmesZE6Egp+6+YMdRj5gOdgYCAD8iRIBmLt4VrowJyQp2TtRIsOMofUKS4l3hlicDgMbjYlWgBUgb3F2X9e2sA6VH1SOmAxECoNUgRIAWIt4VToAAaHV4awYAAFhDiAAAAGsIEQAAYA0hAgAArPEqRDIyMjR48GBFRkaqS5cuuvbaa7Vnz546x1RVVemuu+5STEyMIiIiNH78eBUWFvp1aAAA0Dp4FSI5OTmaMWOGPv74Y23atEnHjx9XamqqKioqPMfMnDlT69atU2ZmprZs2aIjR47o6quvVk1Njd+HBwAALZtjjDG/fFj9vv32W3Xp0kU5OTm67LLL5Ha71blzZ73yyitKS0uTJB06dEiJiYlav369rrzyyl98zbKyMrlcLrndbkVFRfk6GgAACCBfv3836hoRt9stSYqOjpYk5eXl6dixY0pNTfUck5CQoKSkJOXm5tb7GlVVVSorK6uzAQCAtsHnEDHGaNasWbr00kuVlJQkSSouLlZoaKg6duxY59jY2FgVFxfX+zoZGRlyuVyeLTEx0deRAABAC+NziNx555369NNPtXr16l881hgjx3HqfWzu3Llyu92e7eDBg76OBAAAWhifQuSuu+7SG2+8offff1/dunXz7I+Li1N1dbUOHz5c5/iSkhLFxsbW+1phYWGKioqqswEAgLbBqxAxxujOO+9UVlaW3nvvPfXs2bPO4wMHDlS7du20adMmz76ioiLt3LlTKSkp/pkYAAC0Gl790rsZM2Zo1apV+uc//6nIyEjPdR8ul0vh4eFyuVyaNm2a7rvvPnXq1EnR0dGaPXu2kpOTNWrUqCZZAAAAaLm8un33dNd5vPTSS5o6daok6YcfftD999+vVatWqbKyUiNHjtTixYsbfBEqt+8CANDy+Pr9u1E/R6QpECIAALQ8Vn6OCAAAQGMQIgAAwBpCBAAAWEOIAAAAawgRAABgDSECAACsIUQAAIA1hAgAALCGEAEAANYQIgAAwBpCBAAAWEOIAAAAawgRAABgDSECAACsIUQAAIA1hAgAALCGEAEAANYQIgAAwBpCBAAAWEOIAAAAawgRAABgDSECAACsIUQAAIA1hAgAALCGEAEAANYQIgAAwBpCBAAAWEOIAAAAawgRAABgDSECAACsIUQAAIA1hAgAALCGEAEAANYQIgAAtCFF7krl7itVkbvS9iiSpBDbAwAAgMBYs/Urzc3aoVojBTlSxoRkpQ3ubnUmzogAANAGFLkrPREiSbVGejBrp/UzI4QIAABtQEFphSdCTqoxRgdKj9oZ6H8RIgAAtAE9YyIU5NTdF+w46hHTwc5A/4sQAQCgDYh3hStjQrKCnRM1Euw4Sp+QpHhXuNW5uFgVAIA2Im1wd13Wt7MOlB5Vj5gO1iNEIkQAAGhT4l3hzSJATuKtGQAAYA0hAgAArCFEAACANYQIAACwhhABAADWECIAAMAaQgQAAFhDiAAAAGsIEQAAYA0hAgAArPE6RDZv3qxx48YpISFBjuPo9ddfr/O4MUYLFixQQkKCwsPDdfnll2vXrl1+GxgAALQeXodIRUWFBgwYoEWLFtX7+JNPPqmFCxdq0aJF2rp1q+Li4jR69GiVl5c3elgAANC6eP1L78aMGaMxY8bU+5gxRs8++6zmzZunCRMmSJJWrFih2NhYrVq1SrfffnvjpgUAAK2KX68RKSgoUHFxsVJTUz37wsLCNHz4cOXm5tb7nKqqKpWVldXZAABA2+DXECkuLpYkxcbG1tkfGxvreeynMjIy5HK5PFtiYqI/RwIAAM1Yk9w14zhOnY+NMafsO2nu3Llyu92e7eDBg00xEgAAaIa8vkbk58TFxUk6cWYkPj7es7+kpOSUsyQnhYWFKSwszJ9jAACAFsKvZ0R69uypuLg4bdq0ybOvurpaOTk5SklJ8ecfBQAAWgGvz4gcOXJEX3zxhefjgoIC5efnKzo6Wt27d9fMmTOVnp6uPn36qE+fPkpPT1eHDh100003+XVwAADQ8nkdItu2bdOIESM8H8+aNUuSNGXKFC1fvlwPPPCAKisrNX36dB0+fFhDhgzRxo0bFRkZ6b+pAQBAq+AYY4ztIX6srKxMLpdLbrdbUVFRtscBAAAN4Ov3b37XDAAAsIYQAQAA1hAiAADAGkIEAABYQ4gAAABrCBEAAGANIQIAAKwhRAAAgDWECAAAsIYQAQAA1hAiAADAGkIEAABYQ4gAAABrCBEAAGANIQIAAKwhRAAAgDWECAAAsIYQAQAA1hAiAADAGkIEAABYQ4gAAABrCBEAAGANIQIAAKwhRAAAgDWECAAAsIYQAQAA1hAiAADAGkIEANCsFLkrlbuvVEXuStujIABCbA8AAMBJa7Z+pblZO1RrpCBHypiQrLTB3W2PhSbEGREAQLNQ5K70RIgk1RrpwaydnBlp5QgRAECzUFBa4YmQk2qM0YHSo3YGQkAQIgCAZqFnTISCnLr7gh1HPWI62BkIAUGIAACahXhXuDImJCvYOVEjwY6j9AlJineFW54MTYmLVQG0aEXuShWUVqhnTATfsFqBtMHddVnfzjpQelQ9Yjrwd9oGECIAWizusGid4l3hBEgbwlszAFok7rAAWgdCBECLxB0WQOtAiABokbjDAmgdCBEALRJ3WACtAxerAmixuMMCaPkIEQAtGndYAC0bb80AAABrCBEAAGANIQIAAKwhRAAAgDWECAAAsIYQAQAA1hAiAADAGkIEAABYQ4gAAABrCBEAAGANIQIAAKxpdr9rxhgjSSorK7M8CQAAaKiT37dPfh9vqGYXIuXl5ZKkxMREy5MAAABvlZeXy+VyNfh4x3ibLk2strZWhw4dUmRkpBzHkXSishITE3Xw4EFFRUVZnrBptZW1tpV1Sqy1NWor65TazlrbyjqlplurMUbl5eVKSEhQUFDDr/xodmdEgoKC1K1bt3ofi4qKavX/Bzmpray1raxTYq2tUVtZp9R21tpW1ik1zVq9ORNyEherAgAAawgRAABgTfCCBQsW2B6iIYKDg3X55ZcrJKTZvZvkd21lrW1lnRJrbY3ayjqltrPWtrJOqXmttdldrAoAANoO3poBAADWECIAAMAaQgQAAFhDiAAAAGsIEQAAYE2zCZHFixerZ8+eat++vQYOHKgPPvjgtMcuXbpUw4YNU8eOHdWxY0eNGjVK//nPfwI4beN4s9Yfy8zMlOM4uvbaa5t4Qv/wdp3ff/+9ZsyYofj4eLVv317nnHOO1q9fH6BpG8fbtT777LM6++yzFR4ersTERN1777364YcfAjStbzZv3qxx48YpISFBjuPo9ddf/8Xn5OTkaODAgWrfvr169eqlv/71rwGYtPG8XWtWVpZGjx6tzp07KyoqSkOHDtXbb78doGl958vf6UkffvihQkJCdP755zfhhP7jy1qrqqo0b948nXnmmQoLC1Pv3r314osvBmBa3/myzpUrV2rAgAHq0KGD4uPjdcstt+i7774LwLQnNIsQWbNmjWbOnKl58+Zp+/btGjZsmMaMGaOvvvqq3uOzs7N144036v3339dHH32k7t27KzU1VV9//XWAJ/eet2s96csvv9Ts2bM1bNiwAE3aON6us7q6WqNHj9aBAwe0du1a7dmzR0uXLlXXrl0DPLn3vF3rypUrNWfOHM2fP1+7d+/WsmXLtGbNGs2dOzfAk3unoqJCAwYM0KJFixp0fEFBga666ioNGzZM27dv14MPPqi7775br732WhNP2njernXz5s0aPXq01q9fr7y8PI0YMULjxo3T9u3bm3jSxvF2nSe53W5NnjxZI0eObKLJ/M+XtU6cOFHvvvuuli1bpj179mj16tXq169fE07ZeN6uc8uWLZo8ebKmTZumXbt26dVXX9XWrVt16623NvGkP2KagYsuusjccccddfb169fPzJkzp0HPP378uImMjDQrVqxoivH8ype1Hj9+3FxyySXm73//u5kyZYq55pprmnrMRvN2nUuWLDG9evUy1dXVgRjPr7xd64wZM8wVV1xRZ9+sWbPMpZde2mQz+psks27dup895oEHHjD9+vWrs+/22283F198cVOO5ncNWWt9zj33XPPII480wURNw5t1pqWlmYceesjMnz/fDBgwoIkn87+GrPWtt94yLpfLfPfddwGayv8ass6nnnrK9OrVq86+5557znTr1q0pR6vD+hmR6upq5eXlKTU1tc7+1NRU5ebmNug1jh49qmPHjik6OropRvQbX9f6hz/8QZ07d9a0adOaekS/8GWdb7zxhoYOHaoZM2YoNjZWSUlJSk9PV01NTSBG9pkva7300kuVl5fneTtx//79Wr9+vcaOHdvk8wbSRx99dMrn5corr9S2bdt07NgxS1MFRm1trcrLy5v91yRfvPTSS9q3b5/mz59ve5Qm9cYbb2jQoEF68skn1bVrV/Xt21ezZ89WZWWl7dH8KiUlRYWFhVq/fr2MMfrmm2+0du3agH49sv6zXUtLS1VTU6PY2Ng6+2NjY1VcXNyg15gzZ466du2qUaNGNcWIfuPLWj/88EMtW7ZM+fn5gRjRL3xZ5/79+/Xee+9p0qRJWr9+vfbu3asZM2bo+PHjevjhhwMxtk98WesNN9ygb7/9VpdeeqmMMTp+/Lh+//vfa86cOYEYOWCKi4vr/bwcP35cpaWlio+PtzRZ03v66adVUVGhiRMn2h7Fr/bu3as5c+bogw8+aBY/Grwp7d+/X1u2bFH79u21bt06lZaWavr06frvf//b7K8T8UZKSopWrlyptLQ0/fDDDzp+/LjGjx+vv/zlLwGbwfoZkZMcx6nzsTHmlH31efLJJ7V69WplZWWpffv2TTWeXzV0reXl5frtb3+rpUuXKiYmJlDj+Y03f6e1tbXq0qWLXnjhBQ0cOFA33HCD5s2bpyVLlgRi1EbzZq3Z2dl6/PHHtXjxYn3yySfKysrSm2++qUcffTQQowZUfZ+X+va3JqtXr9aCBQu0Zs0adenSxfY4flNTU6ObbrpJjzzyiPr27Wt7nCZXW1srx3G0cuVKXXTRRbrqqqu0cOFCLV++vFWdFfnss89099136+GHH1ZeXp42bNiggoIC3XHHHQGbwXrSxsTEKDg4+JR/PZaUlJzyr6mf+tOf/qT09HS988476t+/f1OO6RfernXfvn06cOCAxo0b59lXW1srSQoJCdGePXvUu3fvph3aB778ncbHx6tdu3YKDg727DvnnHNUXFys6upqhYaGNunMvvJlrf/zP/+jm2++2XMxWHJysioqKnTbbbdp3rx5CgpqNv8+aJS4uLh6Py8hISHq1KmTpama1po1azRt2jS9+uqrzf4MrbfKy8u1bds2bd++XXfeeaekE1+PjDEKCQnRxo0bdcUVV1ie0n/i4+PVtWtXuVwuz75zzjlHxhgVFhaqT58+Fqfzn4yMDF1yySW6//77JUn9+/dXRESEhg0bpsceeywgZy6tf8ULDQ3VwIEDtWnTpjr7N23apJSUlNM+76mnntKjjz6qDRs2aNCgQU09pl94u9Z+/fppx44dys/P92zjx4/XiBEjlJ+fr8TExECN7hVf/k4vueQSffHFF57QkqTPP/9c8fHxzTZCJN/WevTo0VNiIzg4WMYYzxmD1mDo0KGnfF42btyoQYMGqV27dpamajqrV6/W1KlTtWrVqlZ3vY8kRUVFnfL16I477tDZZ5+t/Px8DRkyxPaIfnXJJZfo0KFDOnLkiGff559/rqCgIHXr1s3iZP51uq9HkgL39Shgl8X+jMzMTNOuXTuzbNky89lnn5mZM2eaiIgIc+DAAWOMMTfffHOdOxCeeOIJExoaatauXWuKioo8W3l5ua0lNJi3a/2plnLXjLfr/Oqrr8wZZ5xh7rzzTrNnzx7z5ptvmi5dupjHHnvM1hIazNu1zp8/30RGRprVq1eb/fv3m40bN5revXubiRMn2lpCg5SXl5vt27eb7du3G0lm4cKFZvv27ebLL780xhgzZ84cc/O3oge7AAAE9UlEQVTNN3uO379/v+nQoYO59957zWeffWaWLVtm2rVrZ9auXWtrCQ3m7VpXrVplQkJCzPPPP1/na9L3339vawkN4u06f6ol3TXj7VrLy8tNt27dzPXXX2927dplcnJyTJ8+fcytt95qawkN4u06X3rpJRMSEmIWL15s9u3bZ7Zs2WIGDRpkLrroooDN3CxCxBhjnn/+eXPmmWea0NBQc+GFF5qcnBzPY8OHDzdTpkzxfHzmmWcaSads8+fPD/zgPvBmrT/VUkLEGO/XmZuba4YMGWLCwsJMr169zOOPP26OHz8e4Kl9481ajx07ZhYsWGB69+5t2rdvbxITE8306dPN4cOHLUzecO+//369/92dXNuUKVPM8OHD6zwnOzvbXHDBBSY0NNT06NHDLFmyJPCD+8DbtQ4fPvxnj2+ufPk7/bGWFCK+rHX37t1m1KhRJjw83HTr1s3MmjXLHD16NPDDe8GXdT733HPm3HPPNeHh4SY+Pt5MmjTJFBYWBmxmx5hWdC4YAAC0KNavEQEAAG0XIQIAAKwhRAAAgDWECAAAsIYQAQAA1hAiAADAGkIEAABYQ4gAANAKbN68WePGjVNCQoIcx9Hrr7/u1fOzs7N1zTXXKD4+XhERETr//PO1cuXKOsdkZWVp0KBB+tWvfuU55pVXXmnU3IQIAACtQEVFhQYMGKBFixb59Pzc3Fz1799fr732mj799FP97ne/0+TJk/Wvf/3Lc0x0dLTmzZunjz76SJ9++qluueUW3XLLLXr77bd9npufrAoAQCvjOI7WrVuna6+91rOvurpaDz30kFauXKnvv/9eSUlJeuKJJ3T55Zef9nXGjh2r2NhYvfjii6c95sILL9TYsWP16KOP+jQrZ0QAAGgDbrnlFn344YfKzMzUp59+qt/85jf69a9/rb179572OW63W9HR0fU+ZozRu+++qz179uiyyy7zea4Qn58JAABahH379mn16tUqLCxUQkKCJGn27NnasGGDXnrpJaWnp5/ynLVr12rr1q3629/+Vme/2+1W165dVVVVpeDgYC1evFijR4/2eTZCBACAVu6TTz6RMUZ9+/ats7+qqkqdOnU65fjs7GxNnTpVS5cu1XnnnVfnscjISOXn5+vIkSN69913NWvWLPXq1etn3+L5OYQIAACtXG1trYKDg5WXl6fg4OA6j51xxhl1Ps7JydG4ceO0cOFCTZ48+ZTXCgoK0llnnSVJOv/887V7925lZGQQIgAAoH4XXHCBampqVFJSomHDhp32uOzsbF199dV64okndNtttzXotY0xqqqq8nk2QgQAgFbgyJEj+uKLLzwfFxQUKD8/X9HR0erbt68mTZqkyZMn6+mnn9YFF1yg0tJSvffee0pOTtZVV12l7OxsjR07Vvfcc4+uu+46FRcXS5JCQ0M9F6xmZGRo0KBB6t27t6qrq7V+/Xq9/PLLWrJkic9zc/suAACtQHZ2tkaMGHHK/ilTpmj58uU6duyYHnvsMb388sv6+uuv1alTJw0dOlSPPPKIkpOTNXXqVK1YseKU5w8fPlzZ2dmSpIceekhr1qxRYWGhwsPD1a9fP91zzz1KS0vzeW5CBAAAWMPPEQEAANYQIgAAwBpCBAAAWEOIAAAAawgRAABgDSECAACsIUQAAIA1hAgAALCGEAEAANYQIgAAwBpCBAAAWPP/AfVuaBNiBJfdAAAAAElFTkSuQmCC",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x1330e07f0>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1-element Array{PyCall.PyObject,1}:\n",
       " PyObject <matplotlib.lines.Line2D object at 0x12a7fee10>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot( metal[:n], metal[:mu], \".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x13e01a2e8>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1-element Array{PyCall.PyObject,1}:\n",
       " PyObject <matplotlib.lines.Line2D object at 0x12a729710>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot( intr[:n], intr[:muE], \".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x12a729828>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1-element Array{PyCall.PyObject,1}:\n",
       " PyObject <matplotlib.lines.Line2D object at 0x141536da0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot( metal[:n], metal[:mu], \".\")\n",
    "plot( intr[:n], intr[:muE], \".\")\n",
    "plot( intr[:n], intr[:muH], \".\")\n",
    "plot( siN[:n], siN[:muE], \".-\")\n",
    "plot( siP[:p], siP[:muH], \".-\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x14164def0>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject Text(24,0.5,'mobility (cm^2/Vs)')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot( metal[:n], metal[:mu], \".\")\n",
    "plot( intr[:n], intr[:muE], \".\")\n",
    "plot( intr[:n], intr[:muH], \".\")\n",
    "plot( siN[:n], siN[:muE], \".-\")\n",
    "plot( siP[:p], siP[:muH], \".-\")\n",
    "xscale(\"log\")\n",
    "yscale(\"log\")\n",
    "xlim(2e5, 5e24)\n",
    "ylim(1e0, 1e5)\n",
    "xlabel(\"carrier concentration (cm^-3)\")\n",
    "ylabel(\"mobility (cm^2/Vs)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x1414c59e8>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject Text(31.6,0.5,'mobility (${\\\\rm cm}^{2}/{\\\\rm Vs}$ )')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot( metal[:n], metal[:mu], \".\")\n",
    "plot( intr[:n], intr[:muE], \".\")\n",
    "plot( intr[:n], intr[:muH], \".\")\n",
    "plot( siN[:n], siN[:muE], \".-\")\n",
    "plot( siP[:p], siP[:muH], \".-\")\n",
    "xscale(\"log\")\n",
    "yscale(\"log\")\n",
    "xlim(2e5, 5e24)\n",
    "ylim(1e0, 1e5)\n",
    "xlabel(L\"carrier concentration (${\\rm cm}^{-3}$)\")\n",
    "ylabel(L\"mobility (${\\rm cm}^{2}/{\\rm Vs}$ )\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\int \\frac{1}{x^2} \\text{d}x$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\int \\frac{1}{x_1^2} {\\rm d}\\it{x_1}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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