6. [Modul] Pengenalan Interpolasi dalam Python.ipynb

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    "<div style=\"background-color: #28a745 !important; color: white; min-height: 50px; padding: 10px; margin: auto;\">\n",
    "    <p style=\"text-align: right; font-style: italic\">Praktikum Metode Komputasi-6</p>\n",
    "    <h1>Pengenalan Interpolasi dalam Python</h1>\n",
    "</div>\n",
    "<div style=\"background-color: whitesmoke; padding: 10px \">\n",
    "    <ul>\n",
    "        <li>Tujuan: Memperkenalkan beberapa jenis interpolasi pada Python</li>\n",
    "        <li>Keluaran: Mahasiswa dapat memahami metode-metode interpolasi dana menerapkannya dalam Python</li>\n",
    "        <li>Pertemuan: 6</li>\n",
    "        <li>Waktu/Tempat: Jumat, 25 April 2020/ Youtube</li>\n",
    "    </ul>\n",
    "</div>"
   ]
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    "## 1. Seperti apa interpolasi itu?\n",
    "\n",
    "Interpolasi adalah suatu proses untuk menentukan nilai di suatu titik berdasarkan nilai titik-titik di sekitarnya. Proses ini dilakukan karena saat pengambilan data di lapangan biasnaya kita hanya mengambil sampling-sampling titik-titik tertentu. Nilai di wilayah-wilayah di sekitar tempat kita sampling data dapat ditetntukan dengan menggunakan interpolasi ini. Ada berbagai macam metode interpolasi, dalam praktikum ini kita akan menggunakan 3 macam metode interpolasi yang ada dalam paket `scipy` yaitu `linear`, `nearest`, dan `cubic`.\n",
    "\n",
    "Saat kita melakukan akuisisi di lapangan biasanya saat mendesain titik-titik survei kita dihadapakan pada beberapa batasan yaitu biaya, waktu, dan resolusi. Survei biasanya dilakukan dengan waktu dan biaya yang terbatas, oleh karena itu agar efektif seharusnya kita menentukan jumlah dan spasi titik ukur yang paling optimal dengan resolusi yang cukup. Semakin sedikit titik survei dan semakin lebar jarak antara titik maka biaya dan waktu semakin rendah, tetapi resolusi data menjadi rendah juga. Oleh karena itu kita biasanya mencari jumlah optimal dimana jumlah titik dan spasinya optimal dengan resolusi yang kita harapkan.\n",
    "\n",
    "### 1.1 Pengenalan `pandas` \n",
    "\n",
    "Kita akan melakukan interpolasi dari data lapangan asli yang biasanya digunakan untuk praktikum pemrosesan data non seismik. Data yang didapatkan biasanya berbentuk tabular/tabel seperti Excel dengan nilai umum berupa koordinat dan nilai pengukuran. Untuk praktik ini kita akan simpan data tabular tersebut menjadi format CSV (comma separated values) atau data-data yang terpisahkan oleh tanda koma.\n",
    "\n",
    "Nah data tersebut kemudian akan kita load ke Python, ada banyak cara untuk melakukannya, salah satunya menggunakan `pandas`, paket ini sendiri biasanya digunakan untuk pengolahan data tabular.... Nah apabila teman-teman belum menginstalnya maka dapat diinstall terlebih dahulu.\n",
    "\n",
    "Untuk meload data kita dapat menggunakan fungsi `load_csv`:"
   ]
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   "cell_type": "code",
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   "source": [
    "import pandas as pd\n",
    "\n",
    "data_gravitasi = pd.read_csv('./material/data_gravitasi.csv', delimiter = ',')"
   ]
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      "Help on package pandas:\n",
      "\n",
      "NAME\n",
      "    pandas\n",
      "\n",
      "DESCRIPTION\n",
      "    pandas - a powerful data analysis and manipulation library for Python\n",
      "    =====================================================================\n",
      "    \n",
      "    **pandas** is a Python package providing fast, flexible, and expressive data\n",
      "    structures designed to make working with \"relational\" or \"labeled\" data both\n",
      "    easy and intuitive. It aims to be the fundamental high-level building block for\n",
      "    doing practical, **real world** data analysis in Python. Additionally, it has\n",
      "    the broader goal of becoming **the most powerful and flexible open source data\n",
      "    analysis / manipulation tool available in any language**. It is already well on\n",
      "    its way toward this goal.\n",
      "    \n",
      "    Main Features\n",
      "    -------------\n",
      "    Here are just a few of the things that pandas does well:\n",
      "    \n",
      "      - Easy handling of missing data in floating point as well as non-floating\n",
      "        point data.\n",
      "      - Size mutability: columns can be inserted and deleted from DataFrame and\n",
      "        higher dimensional objects\n",
      "      - Automatic and explicit data alignment: objects can be explicitly aligned\n",
      "        to a set of labels, or the user can simply ignore the labels and let\n",
      "        `Series`, `DataFrame`, etc. automatically align the data for you in\n",
      "        computations.\n",
      "      - Powerful, flexible group by functionality to perform split-apply-combine\n",
      "        operations on data sets, for both aggregating and transforming data.\n",
      "      - Make it easy to convert ragged, differently-indexed data in other Python\n",
      "        and NumPy data structures into DataFrame objects.\n",
      "      - Intelligent label-based slicing, fancy indexing, and subsetting of large\n",
      "        data sets.\n",
      "      - Intuitive merging and joining data sets.\n",
      "      - Flexible reshaping and pivoting of data sets.\n",
      "      - Hierarchical labeling of axes (possible to have multiple labels per tick).\n",
      "      - Robust IO tools for loading data from flat files (CSV and delimited),\n",
      "        Excel files, databases, and saving/loading data from the ultrafast HDF5\n",
      "        format.\n",
      "      - Time series-specific functionality: date range generation and frequency\n",
      "        conversion, moving window statistics, date shifting and lagging.\n",
      "\n",
      "PACKAGE CONTENTS\n",
      "    _config (package)\n",
      "    _libs (package)\n",
      "    _testing\n",
      "    _typing\n",
      "    _version\n",
      "    api (package)\n",
      "    arrays (package)\n",
      "    compat (package)\n",
      "    conftest\n",
      "    core (package)\n",
      "    errors (package)\n",
      "    io (package)\n",
      "    plotting (package)\n",
      "    testing\n",
      "    tests (package)\n",
      "    tseries (package)\n",
      "    util (package)\n",
      "\n",
      "SUBMODULES\n",
      "    _hashtable\n",
      "    _lib\n",
      "    _tslib\n",
      "    offsets\n",
      "\n",
      "FUNCTIONS\n",
      "    __getattr__(name)\n",
      "\n",
      "DATA\n",
      "    IndexSlice = <pandas.core.indexing._IndexSlice object>\n",
      "    NA = <NA>\n",
      "    NaT = NaT\n",
      "    __docformat__ = 'restructuredtext'\n",
      "    __git_version__ = '3adf3340453d6704d4a2cb47058214cc697a7d29'\n",
      "    describe_option = <pandas._config.config.CallableDynamicDoc object>\n",
      "    get_option = <pandas._config.config.CallableDynamicDoc object>\n",
      "    options = <pandas._config.config.DictWrapper object>\n",
      "    reset_option = <pandas._config.config.CallableDynamicDoc object>\n",
      "    set_option = <pandas._config.config.CallableDynamicDoc object>\n",
      "\n",
      "VERSION\n",
      "    1.0.3\n",
      "\n",
      "FILE\n",
      "    /media/anangsahroni/CADC45ABDC45929D/Academic/Metkom2020/2020_Praktikum-Metode-Komputasi/metkom/lib/python3.7/site-packages/pandas/__init__.py\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pd)"
   ]
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       "      <th></th>\n",
       "      <th>Nama titik</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>gobs</th>\n",
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       "   Nama titik           x            y        gobs\n",
       "0      MG 001  468382.920  9281849.895  978123.886\n",
       "1      MG 002  466304.082  9283087.659  978127.388\n",
       "2      MG 003  469464.082  9283339.411  978125.695\n",
       "3      MG 004  470596.486  9283051.793  978123.132\n",
       "4      MG 005  472685.611  9280573.340  978116.323\n",
       "..        ...         ...          ...         ...\n",
       "92     MG 093  477310.018  9289146.779  978126.725\n",
       "93     MG 094  479978.790  9284259.277  978115.840\n",
       "94     MG 095  482697.804  9287586.288  978124.347\n",
       "95     MG 096  481910.182  9291611.169  978129.657\n",
       "96     MG 097  484775.528  9285029.334  978120.342\n",
       "\n",
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   ],
   "source": [
    "data_gravitasi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nah tabel di atas atau `data_gravitasi` merupakan sebuah `DataFrame` atau data-data yang ada di dalam sebuuah frame tertentu, data-data disini maksudnya adalah data perkolom, masing-masing kolom tersebut sebenarnya merupakan Array 1D. Bagaimana cara memanggil salah satu kolomnya?\n",
    "\n",
    "Kita dapat menggunakan `loc` ataupun `iloc` seperti contoh di bawah ini:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_g shape is:  (97,)\n",
      "\n",
      "x_g: \n",
      " 0     468382.920\n",
      "1     466304.082\n",
      "2     469464.082\n",
      "3     470596.486\n",
      "4     472685.611\n",
      "         ...    \n",
      "92    477310.018\n",
      "93    479978.790\n",
      "94    482697.804\n",
      "95    481910.182\n",
      "96    484775.528\n",
      "Name: x, Length: 97, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "x_g = data_gravitasi.loc[:, 'x']\n",
    "print('x_g shape is: ', x_g.shape)\n",
    "\n",
    "print('\\nx_g: \\n', x_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     468382.920\n",
      "1     466304.082\n",
      "2     469464.082\n",
      "3     470596.486\n",
      "4     472685.611\n",
      "         ...    \n",
      "92    477310.018\n",
      "93    479978.790\n",
      "94    482697.804\n",
      "95    481910.182\n",
      "96    484775.528\n",
      "Name: x, Length: 97, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "x_gi = data_gravitasi.iloc[:,1]\n",
    "print(x_gi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nah jadi DataFrame berfungsi sebagai wadah array-array 1D tersebut menjadi satu bagian yang saling berhubungan. Karena setiap kolom merupakan Array 1D kita bisa coba plot koordinat x dan koordinat y menggunakan `matplotlib.pyplot.scatter` seperti di bawah ini:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x_g = data_gravitasi.loc[:, 'x']\n",
    "y_g = data_gravitasi.loc[:, 'y']\n",
    "\n",
    "plt.scatter(x_g, y_g, label=\"titik pengukuran\")\n",
    "plt.title('Titik Pengukuran Metode Gravitasi')\n",
    "plt.xlabel('Easting (m)')\n",
    "plt.ylabel('Northing (m)')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sebenarnya masih banyak sekali kegunaan dari `pandas` biasanya berkaitan dengan data tabular.\n",
    "\n",
    "### 1.2 Interpolasi dalam `scipy`\n",
    "\n",
    "Untuk interpolasi dalam Python ada berbagai macam cara, yang akan kita gunakan pada praktikum ini adalah berdasarkan modul `scipy` atau scientific python. Salah satu fungsi dalam modul ini adalah untuk interpolasi, fungsi tersebut adalah `scipy.interpolate.griddata`.\n",
    "\n",
    "Untuk interpolasi menggunakan `griddata` kita perlu membuat koordinat teratur atau grid seperti pada praktikum kemarin. Nah nilai di setiap titik di dalam grid tersebut akan dicari menggunakan interpolasi dari titik-titik pengukuran yang sudah ada. Proses tersebut tidak secara manual kita lakukan menggunakan `meshgrid` seperti kemarin, tetapi secara otomatis dilakukan di dalam `griddata`. Namun kita akan coba visualisasikan dengan membuat penggambaran secara manual.\n",
    "\n",
    "Mula-mula kita akan membuat koordinat x dan y sebagai modal untuk membuat grid. Koordinat x dan y ini kita dapatkan dengan `np.linspace` dari nilai x maupun y minimum (`np.min(x_g)` atau `np.min(y_g)`) sampai ke maksimumnya dengan jumlah titik sesuai keinginan kita, pada kasus ini kita ambil jumlah titik 200 sehingga spasi grid antar titik menjadi lebar sebaran data (xmax-xmin) dan (ymax-ymin) dibagi 200."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "xmin = np.min(x_g)\n",
    "xmax = np.max(x_g)\n",
    "\n",
    "ymin = np.min(y_g)\n",
    "ymax = np.max(y_g)\n",
    "\n",
    "x_g_grid = np.arange(xmin, xmax, 2000)\n",
    "y_g_grid = np.arange(ymin, ymax, 2000)\n",
    "\n",
    "X_g_grid, Y_g_grid = np.meshgrid(x_g_grid, y_g_grid)\n",
    "\n",
    "plt.scatter(X_g_grid, Y_g_grid, color=\"black\")\n",
    "plt.scatter(x_g, y_g, color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gambar di atas hanyalah gambaran bahwa kita akan memperkirakan nilai-nilai di titik-titik hitam berdasarkan data pengukuran kita di titik-titik merah. Kegiatan memperkirakan inilah yang kita sebut sebagai interpolasi. Nah cara interpolasi sendiri sebagai berikut:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.interpolate import griddata\n",
    "\n",
    "# data koordinat dan nilai pengukuran\n",
    "x_g = data_gravitasi.loc[:, 'x']\n",
    "y_g = data_gravitasi.loc[:, 'y']\n",
    "gobs_g = data_gravitasi.loc[:, 'gobs']\n",
    "\n",
    "xmin = np.min(x_g)\n",
    "xmax = np.max(x_g)\n",
    "\n",
    "ymin = np.min(y_g)\n",
    "ymax = np.max(y_g)\n",
    "\n",
    "x_g_grid = np.arange(xmin, xmax, 200)\n",
    "y_g_grid = np.arange(ymin, ymax, 200)\n",
    "\n",
    "#gobs_g_grid = griddata((x_g, y_g), gobs_g, (x_g_grid[None,:], y_g_grid[:, None]), method='cubic')\n",
    "gobs_g_grid = griddata((x_g, y_g), gobs_g, (x_g_grid[None,:], y_g_grid[:, None]), method='cubic')\n",
    "\n",
    "\n",
    "plt.pcolormesh(x_g_grid, y_g_grid, gobs_g_grid)\n",
    "plt.colorbar()\n",
    "plt.contour(x_g_grid, y_g_grid, gobs_g_grid, levels=14, linewidths=0.5, colors='k')\n",
    "plt.scatter(x_g, y_g, s=10, alpha=0.8, color=\"black\")\n",
    "\n",
    "plt.title('Medan Gravitasi Observasi (mgal)')\n",
    "\n",
    "plt.xlabel('Easting (m)')\n",
    "plt.ylabel('Northing (m)')\n",
    "\n",
    "plt.show()\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[468382.92 , 466304.082, 469464.082, 470596.486, 472685.611,\n",
       "        474073.812, 475630.214, 474391.455, 472936.332, 471042.91 ,\n",
       "        473607.583, 475553.895, 475888.785, 478157.236, 476364.206,\n",
       "        471515.943, 468726.214, 467469.479, 467115.368, 469538.344,\n",
       "        461283.633, 465214.243, 467824.719, 470708.856, 473741.028,\n",
       "        475932.387, 478033.248, 478882.986, 479463.054, 480564.415,\n",
       "        472682.814, 474653.577, 476014.556, 474973.466, 472295.654,\n",
       "        469075.476, 476649.273, 479264.071, 480670.474, 480845.818,\n",
       "        479565.575, 479051.6  , 480582.588, 481682.811, 481572.256,\n",
       "        482196.527, 483457.378, 484608.307, 481141.706, 474771.127,\n",
       "        482840.233, 483859.731, 484255.384, 485294.466, 485755.43 ,\n",
       "        486261.588, 485864.977, 485935.087, 488000.051, 490373.194,\n",
       "        491569.835, 494798.793, 491483.911, 489544.002, 489840.359,\n",
       "        487536.984, 487836.984, 489378.399, 491033.507, 493439.796,\n",
       "        494162.09 , 496400.287, 498365.524, 497868.22 , 497135.772,\n",
       "        495033.529, 497368.069, 468045.287, 466987.029, 464499.915,\n",
       "        465622.053, 469340.059, 467520.054, 466980.59 , 462501.241,\n",
       "        460915.534, 465030.378, 463040.698, 470056.186, 471340.976,\n",
       "        477382.846, 478333.675, 477310.018, 479978.79 , 482697.804,\n",
       "        481910.182, 484775.528]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_g[None,:]\n",
    "\n",
    "#x_g[:, None]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background-color: #ffc107  !important; color: grey; min-height: 50px; padding: 10px; margin: auto;\">\n",
    "    <p style=\"text-align: right; font-style: italic\">Praktikum Metode Komputasi-6</p>\n",
    "    <h1>Tugas 6: Pengenalan Interpolasi dalam Python</h1>\n",
    "</div>\n",
    "<div style=\"background-color: whitesmoke; padding: 10px; padding-bottom: 0px; \">\n",
    "    <ul>\n",
    "        <li>Praktikum Metode Komputasi</li>\n",
    "        <li>Pertemuan: 6</li>\n",
    "        <li>Format file: Portable Document Format (PDF)</li>\n",
    "        <li>Template:<a href=\"./material/report_template.docx\"> Download Template Laporan</a></li>\n",
    "        <li>Link Pengumpulan: Google Classroom</li>\n",
    "        <li>Format Nama File: 6-[NIM]-Nama, contoh: 6-362705-Anang Sahroni</li>\n",
    "        <li>Data Magnetik: <a href=\"./material/data_magnetik_praktikum.csv\">Download Data</a></li>\n",
    "    </ul>\n",
    "</div>\n",
    "<div style=\"background-color: #dc3545; color: white \">\n",
    "    <ul>\n",
    "        <li>Deadline: Rabu, 29 April 2020 17.00 WIB, dijadikan 1 dengan semua laporan terdahulu</li>\n",
    "    </ul>\n",
    "</div>\n",
    "\n",
    "<p> Teman-teman buatlah peta seperti di bawah ini menggunakan data sampel yang dapat diunduh pada link di atas, laporan paling lambat hari Rabu, 29 April 2020 dijadikan 1 file dengan semua laporan terdahulu. Karena waktu terbatas untuk laporan ini cukup tuliskan kode keseluruhan, hasil gambar, dan pembahasan secara singkat(tidak perlu rinci perbagian). </p>\n",
    "\n",
    "![H observasi](./material/hobs.png)"
   ]
  }
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