5 [Modul] Array dan Gridding Sederhana dalam Python.ipynb

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   "cell_type": "markdown",
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   "source": [
    "<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-5</p>\n",
    "    <h1> Array dan Gridding Sederhana dalam Python</h1>\n",
    "</div>\n",
    "<div style=\"background-color: whitesmoke; padding: 10px \">\n",
    "    <ul>\n",
    "        <li>Tujuan: Memperkenalkan aplikasi array dalam Python untuk Geofisika</li>\n",
    "        <li>Keluaran: Mahasiswa dapat memahami tentang 2D array dan penggunaannya dalam Geofisika</li>\n",
    "        <li>Pertemuan: 5</li>\n",
    "        <li>Waktu/Tempat: Jumat, 18 April 2020/ Youtube</li>\n",
    "    </ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Mendalami `meshgrid`\n",
    "Kita akan mencoba mempraktekkan mengenai grid seperti pada ilustrasi di bawah ini (sumber: [python-course.eu](http://python-course.eu)). Fungsi `meshgrid` berguna untuk membuat titik teratur yang didapatkan dari 2 buah array 1 Dimensi. Maksudnya gimana? bisa dilihat di bawah ini.\n",
    "\n",
    "Misalkan kita memiliki dua buah array 1 D:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x0= [1 2 3 4]\n",
      "y0= [2 4 6 8]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x0 = np.array([1,2,3,4])\n",
    "y0 = np.array([2,4,6,8])\n",
    "\n",
    "print(\"x0=\", x0)\n",
    "print(\"y0=\", y0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oke sebelum mulai memakai `meshgrid` ada baiknya kita coba buka `help` atau bantuannya dulu:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function meshgrid in module numpy:\n",
      "\n",
      "meshgrid(*xi, **kwargs)\n",
      "    Return coordinate matrices from coordinate vectors.\n",
      "    \n",
      "    Make N-D coordinate arrays for vectorized evaluations of\n",
      "    N-D scalar/vector fields over N-D grids, given\n",
      "    one-dimensional coordinate arrays x1, x2,..., xn.\n",
      "    \n",
      "    .. versionchanged:: 1.9\n",
      "       1-D and 0-D cases are allowed.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x1, x2,..., xn : array_like\n",
      "        1-D arrays representing the coordinates of a grid.\n",
      "    indexing : {'xy', 'ij'}, optional\n",
      "        Cartesian ('xy', default) or matrix ('ij') indexing of output.\n",
      "        See Notes for more details.\n",
      "    \n",
      "        .. versionadded:: 1.7.0\n",
      "    sparse : bool, optional\n",
      "        If True a sparse grid is returned in order to conserve memory.\n",
      "        Default is False.\n",
      "    \n",
      "        .. versionadded:: 1.7.0\n",
      "    copy : bool, optional\n",
      "        If False, a view into the original arrays are returned in order to\n",
      "        conserve memory.  Default is True.  Please note that\n",
      "        ``sparse=False, copy=False`` will likely return non-contiguous\n",
      "        arrays.  Furthermore, more than one element of a broadcast array\n",
      "        may refer to a single memory location.  If you need to write to the\n",
      "        arrays, make copies first.\n",
      "    \n",
      "        .. versionadded:: 1.7.0\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    X1, X2,..., XN : ndarray\n",
      "        For vectors `x1`, `x2`,..., 'xn' with lengths ``Ni=len(xi)`` ,\n",
      "        return ``(N1, N2, N3,...Nn)`` shaped arrays if indexing='ij'\n",
      "        or ``(N2, N1, N3,...Nn)`` shaped arrays if indexing='xy'\n",
      "        with the elements of `xi` repeated to fill the matrix along\n",
      "        the first dimension for `x1`, the second for `x2` and so on.\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    This function supports both indexing conventions through the indexing\n",
      "    keyword argument.  Giving the string 'ij' returns a meshgrid with\n",
      "    matrix indexing, while 'xy' returns a meshgrid with Cartesian indexing.\n",
      "    In the 2-D case with inputs of length M and N, the outputs are of shape\n",
      "    (N, M) for 'xy' indexing and (M, N) for 'ij' indexing.  In the 3-D case\n",
      "    with inputs of length M, N and P, outputs are of shape (N, M, P) for\n",
      "    'xy' indexing and (M, N, P) for 'ij' indexing.  The difference is\n",
      "    illustrated by the following code snippet::\n",
      "    \n",
      "        xv, yv = np.meshgrid(x, y, sparse=False, indexing='ij')\n",
      "        for i in range(nx):\n",
      "            for j in range(ny):\n",
      "                # treat xv[i,j], yv[i,j]\n",
      "    \n",
      "        xv, yv = np.meshgrid(x, y, sparse=False, indexing='xy')\n",
      "        for i in range(nx):\n",
      "            for j in range(ny):\n",
      "                # treat xv[j,i], yv[j,i]\n",
      "    \n",
      "    In the 1-D and 0-D case, the indexing and sparse keywords have no effect.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    index_tricks.mgrid : Construct a multi-dimensional \"meshgrid\"\n",
      "                     using indexing notation.\n",
      "    index_tricks.ogrid : Construct an open multi-dimensional \"meshgrid\"\n",
      "                     using indexing notation.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> nx, ny = (3, 2)\n",
      "    >>> x = np.linspace(0, 1, nx)\n",
      "    >>> y = np.linspace(0, 1, ny)\n",
      "    >>> xv, yv = np.meshgrid(x, y)\n",
      "    >>> xv\n",
      "    array([[0. , 0.5, 1. ],\n",
      "           [0. , 0.5, 1. ]])\n",
      "    >>> yv\n",
      "    array([[0.,  0.,  0.],\n",
      "           [1.,  1.,  1.]])\n",
      "    >>> xv, yv = np.meshgrid(x, y, sparse=True)  # make sparse output arrays\n",
      "    >>> xv\n",
      "    array([[0. ,  0.5,  1. ]])\n",
      "    >>> yv\n",
      "    array([[0.],\n",
      "           [1.]])\n",
      "    \n",
      "    `meshgrid` is very useful to evaluate functions on a grid.\n",
      "    \n",
      "    >>> import matplotlib.pyplot as plt\n",
      "    >>> x = np.arange(-5, 5, 0.1)\n",
      "    >>> y = np.arange(-5, 5, 0.1)\n",
      "    >>> xx, yy = np.meshgrid(x, y, sparse=True)\n",
      "    >>> z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2)\n",
      "    >>> h = plt.contourf(x,y,z)\n",
      "    >>> plt.show()\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(np.meshgrid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oke mari kita coba menggunakan `meshgrid` dan kita lihat apa yang kita dapatkan :D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X0 = \n",
      " [[1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]] \n",
      "X0 shape = (4, 4)\n",
      "\n",
      "Y0 =\n",
      " [[2 2 2 2]\n",
      " [4 4 4 4]\n",
      " [6 6 6 6]\n",
      " [8 8 8 8]] \n",
      "X0 shape = (4, 4)\n"
     ]
    }
   ],
   "source": [
    "X0, Y0 = np.meshgrid(x0, y0)\n",
    "print(\"X0 = \\n\", X0, \"\\nX0 shape =\", X0.shape)\n",
    "print(\"\\nY0 =\\n\", Y0, \"\\nX0 shape =\", Y0.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari contoh di atas kita mendapatkan matriks atau Array 2D masing-masing untuk `X0` dan `Y0`. Apabila teman-teman cermati maka menimbulkan tanda tanya hehe, kok bisa ya jadi seperti itu?\n",
    "\n",
    "Nah untuk itu kita bisa mencoba menggambarkan apa yang terjadi menggunakan matplotlib. Disini saya menggunakan `nol_palsu` maksudnya untuk digunakan sebagai koordinat di x=0 untuk `y0` dan y=0 untuk `x0`, jadi hanya sebagai bantuan visualisasi saja. Kita akan bandingkan hasil plot masing-masing untuk `x0` `y0` dan `X0` `Y0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
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JzXNTtq9T+/95ut9EBd/EeCRyKbKXEcqlZnuqc3tS0q2Svrll2SOSfrNz+05JX4zOs1hZFNnHluc7Dqr93EI6ETEXEXsjYp/aTxR+MSLu2rKs1Ex6vmdiFWx/Su1n3XfZflHSfWo/uaCI+Kikz6l9hcFzkv5H0j3DmLOIAnu5U9Jv2z4tqSXpULbfRB03SPp1SSud84iS9CFJPymNXC5F9jIquVwl6QHbY2r/YfJgRDxq+08kNSLiEbX/UPo728+p/cT2oeGNu60i+3if7YOSTqu9j7uHNu1FqDITXkIOAMllPfUBAOigqAEgOYoaAJKjqAEgOYoaAJKjqAEgOYoaAJL7f0OgPnc+NTv3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt #import matplotlib untuk mengeplot\n",
    "\n",
    "nol_palsu = np.array([0,0,0,0]) #data buatan hanya untuk visualisasi\n",
    "\n",
    "plt.scatter(X0, Y0) #1. plot hasil meshgrid\n",
    "#plt.scatter(x0, nol_palsu) # 2. plot array x0\n",
    "#plt.scatter(nol_palsu, y0, label=\"nol\") # 3. plot array y0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nah dari gambar di atas seharusnya sudah mulai paham, kalo belum bisa lihat video youtube karena ada penjelasan disitu. Jadi maksudnya adalah titik biru merupakan hasil `meshgrid` yang membentuk titik-titik teratur yang berdasarkan dari `x0` dan `y0` kita tadi. Salah satu penjelasan yang bagus dapat kita dapatkan di web [python-course.eu](https://www.python-course.eu/matplotlib_contour_plot.php). Dan beginilah penggambarannya, akan kita ulangi sekali lagi dengan contoh dari web ini :D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://www.python-course.eu/images/creating_a_meshgrid.png\" width=500px title=\"how meshgrid works (python-course.eu\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari gambar di atas kita dapat melihat bahwa untuk `xlist` nilai akan diduplikasi menjadi 4 baris, mengapa? karena menyesuaikan jumlah anggota dari `ylist` yang ada 4 (-3,-1,1,3), sedangkan `ylist` akan ditranspose dulu menjadi \"berdiri\" kemudian diduplikasi 3 kali menyesuaikan jumlah anggota `xlist`. Nah apabila digabungkan maka membentuk koorinat yang bentuknya adalah 3x4, masing-masing dari jumlah anggota `xlist` dan `ylist`.\n",
    "\n",
    "Oke mari kita praktekkan :D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "xlist = np.linspace(-3, 3, 3)\n",
    "ylist = np.linspace(-3, 3, 4)\n",
    "Xlist, Ylist = np.meshgrid(xlist, ylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XList = \n",
      " [[-3.  0.  3.]\n",
      " [-3.  0.  3.]\n",
      " [-3.  0.  3.]\n",
      " [-3.  0.  3.]] \n",
      "Xlist shape = (4, 3)\n",
      "\n",
      "Ylist =\n",
      " [[-3. -3. -3.]\n",
      " [-1. -1. -1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 3.  3.  3.]] \n",
      "Xlist shape = (4, 3)\n"
     ]
    }
   ],
   "source": [
    "print(\"XList = \\n\", Xlist, \"\\nXlist shape =\", Xlist.shape)\n",
    "print(\"\\nYlist =\\n\", Ylist, \"\\nXlist shape =\", Ylist.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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paVzSgqSjSSaHW9VgbH9S0j9IGpH0cJIHh1zSmtj+jqSPaenXpf5U0gNJHhpqUWtg+/cl/aekY1r6WZekv03yxPCqWhvbt0n6tpZ66xpJ30vy1dbmrxjkAID3lDy1AgB4D0EOAMUR5ABQHEEOAMUR5ABQHEEOAMUR5ABQ3P8Dzd6XvNF98Q4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Xlist, Ylist)\n",
    "plt.scatter(0,0, color=\"red\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Membuat kontur sederhana\n",
    "Pada kasus ini saya membuat 2D array baru berupa nilai jarak dari titik (0,0), dengan rumus jarak seperti biasanya."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "Jarak = np.sqrt((Xlist-0)**2 + (Ylist-0)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mengapa kita membuat `Jarak`? Karena kita akan menggunakan nilai tersebut untuk membuat kontur yang nilainya merupakan jarak setiap titik terhadap koordinat (0,0).\n",
    "\n",
    "Nah untuk membuat kontur kita membutuhkan koordinat teratur (grid), makanya tadi kita membuat grid dulu menggunakan `meshgrid`. Fungsi untuk mengeplot kontur sendiri ada beberapa di Python, bisa menggunakan `contour` ataupun `contourf`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm #color map\n",
    "\n",
    "plt.xlim(-4,4) #batas gambar untuk sumbu x\n",
    "plt.ylim(-4,4) #batas gambar untuk sumbu y\n",
    "\n",
    "plt.contourf(Xlist,Ylist,Jarak, cmap=cm.coolwarm) # plot kontur\n",
    "\n",
    "plt.colorbar() # plot skala warna\n",
    "plt.scatter(Xlist,Ylist, color=\"black\") #plot titik data\n",
    "plt.scatter(0,0, color=\"magenta\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Membuat sayatan dari kontur\n",
    "Nah di bawah ini kita akan memanfaatkan numpy untuk membuat sayatan barat-timur dari peta di atas. Kita akan memilih sayatan di y=-1. Bagaimana caranya?\n",
    "\n",
    "Caranya adalah mengambil data `Xlist` dan `Ylist` pada nilai y=-1 memanfaatkan indeksing pada array. Dapat dilihat pada gambar di atasnya lagi tentang konsep meshgrid. Dari gambar di atas maka kita perlu mengambil koordinat (-3,-1), (0,-1), dan (3,-1). Koordinat x yang kita butuhkan berarti adalah (-3, 0, 3) sedangkan oordinat y yang dibutuhkan adalah (-1,-1,-1). Nah kita tinggal cocokkan kedua nilai tersebut dengan indeks dari array `Xlist` dan `Ylist`.\n",
    "\n",
    "Kita coba lihat nilai `Xlist`:\n",
    "```python\n",
    "Xlist = \n",
    " [[-3.  0.  3.]\n",
    " [-3.  0.  3.]\n",
    " [-3.  0.  3.]\n",
    " [-3.  0.  3.]] \n",
    "```\n",
    "Nah kita pasti bingung, terus kita harus ambil yang mana ya? Karena semuanya sama hahahahah. Okee kita bisa ambil baris dimana nilai y=-1. Untuk itu kita lihat dahulu saja untuk `Ylist`.\n",
    "\n",
    "```python\n",
    "Ylist =\n",
    " [[-3. -3. -3.]\n",
    " [-1. -1. -1.]\n",
    " [ 1.  1.  1.]\n",
    " [ 3.  3.  3.]] \n",
    "```\n",
    "\n",
    "Nah disini jelas bahwa kita perlu mengambil baris kedua, atau indeks 1. Untuk itu di `Xlist` yang kita ambil adalah indeks 1 juga.\n",
    "\n",
    "Jadinya seperti ini:\n",
    "```python\n",
    "\n",
    "Xlist[1,:]\n",
    "```\n",
    "dan\n",
    "```python\n",
    "Ylist[1,:]\n",
    "```\n",
    "\n",
    "Maksudnya adalah kita mengambil baris kedua (indeks dimulai dari 0), dari baris kedua tersebut kita ambil semua bagiannya (`:`). Masih ingat? heheeh\n",
    "\n",
    "Untuk mengeplotnya pakailah fungsi:\n",
    "```python\n",
    "plt.plot(Xlist[1,:], Ylist[1,:], color=\"blue\")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2eab80fe10>]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlim(-4,4) #batas gambar untuk sumbu x\n",
    "plt.ylim(-4,4) #batas gambar untuk sumbu y\n",
    "plt.contourf(Xlist,Ylist,Jarak, cmap=cm.coolwarm) # plot kontur\n",
    "plt.colorbar(cmap=cm.coolwarm) # plot skala warna\n",
    "\n",
    "\n",
    "plt.scatter(Xlist,Ylist, color=\"black\") #plot titik data\n",
    "plt.scatter(0,0, color=\"m\")\n",
    "\n",
    "# Nah disini kita masukkan plotnya.\n",
    "plt.plot(Xlist[1,:], Ylist[1,:], color=\"blue\")#plot garis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nah pada gambar di atas kita sudah berhasil mengeplot garis sayatan berwarna biru, untuk menyayat timur-barat pada y=-1.\n",
    "\n",
    "Nah dibawah ini kita akan ambil nilai dari jaraknya untuk grafik sayatannya, caranya simpel. Kita tinggal mengambil nilai `Jarak` dengan indeks sama dengan saat kita mengambil `Xlist` dan `Ylist` yaitu:\n",
    "\n",
    "```python\n",
    "Jarak[1,:]\n",
    "```\n",
    "\n",
    "Nah selanjutnya kita plot sayatannya, karena pada sayatan barat-timur, maka kita hanya perlu nilai `Xlist` saja sebgai sumbu X."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "plt.plot(Xlist[1,:], Jarak[1,:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nah agar lebih jelas kita bisa sandingkan antara kontur dengan grafik di atas menggunakan `subplot` dari `matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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"
    }
   ],
   "source": [
    "plt.subplot(2,1,1)\n",
    "plt.xlim(-4,4) #batas gambar untuk sumbu x\n",
    "plt.ylim(-4,4) #batas gambar untuk sumbu y\n",
    "plt.contourf(Xlist,Ylist,Jarak, cmap=cm.coolwarm) # plot kontur\n",
    "plt.scatter(Xlist,Ylist, color=\"black\") #plot titik data\n",
    "# Nah disini kita masukkan plotnya.\n",
    "plt.plot(Xlist[1,:], Ylist[1,:], color=\"blue\")#plot garis\n",
    "\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(Xlist[1,:], Jarak[1,:])\n",
    "plt.xlim(-4,4) #batas gambar untuk sumbu x\n",
    "plt.ylim(-4,4) #batas gambar untuk sumbu y\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selamat kita sudah berhasil, tampilanya masih aneh tapi sementara seperti ini dulu saja :D\n",
    "\n",
    "Sedikit penjelasan mengenai `subplot`:\n",
    "Fungsi ini berguna untuk membuat plot yang berisi beberapa plot. Pada fungsi di atas terdapat dua baris dan 1 kolom plot, oleh karena itu di `subplot` kita masukkan dua angka pertama yaitu 2 dan 1 yang masing-masing mewakili jumlah baris dan kolom. Angka terakhir merupakan urutan dari plotnya.\n",
    "\n",
    "![sayatan 2](./material/contoh5.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Contoh aplikasinya di Geofisika"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nah disini kita menggunakan contoh pada penghitungan nilai gravitasi di suatu wilayah yang disebabkan oleh adanya benda berbentuk bola dengan jari-jari, densitas, koordinat, dan kedalaman tertentu. Contoh disini saya ambil dari modul Pak Wiwit tentang kuliah Metkom, selengkapnya disini: [Modul Kuliah Metode Komputasi](https://github.com/maswiet/Kuliah_Metode_Komputasi).\n",
    "\n",
    "![bola](./material/bola.png)\n",
    "\n",
    "Rumus untuk menghitung pengaruh dari bola tersebut adalah:\n",
    "\n",
    "<img src=\"https://latex.codecogs.com/svg.latex?\\Large&space;\\vec{g}(F_1)=-\\gamma \\frac{\\frac{4}{3} \\pi a^2 \\rho}{R^2}\\hat{r}\" title=\"\\Large x=\\frac{-b\\pm\\sqrt{b^2-4ac}}{2a}\" />\n",
    "\n",
    "\n",
    "Tenang tidak usah panik hehe, rumusnya tinggal kita pakai saja dan kita masuk-masukkan nilai parameternya.\n",
    "\n",
    "### 4.1 Mengimpor modul dan membuat konstanta-konstanta yang diperlukan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#```--------Program menghitung gaya tarik gravitasi-------------------```\n",
    "#```               benda berbentuk bola                              ```\n",
    "#```------------------------------------------------------------------```\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm\n",
    "import numpy as np\n",
    "\n",
    "gamma = 6.6E-11 # konstanta gravitasi\n",
    "si2mg = 1.0E5 # konversi satuan\n",
    "pi = 3.14159265 # phi\n",
    "km2m = 1e3 # konversi satuan\n",
    "ierror = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selanjutnya kita membuat fungsinya sesuai dengan rumus di atas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bola(xq,yq,zq,a,rho,xp,yp,zp):\n",
    "    #``` titik pengamatan terletak pada koordinat (xp,yp,zp), sementara pusat bola adalah di (xq,yq,zq). Jari-jari bola adalah a\n",
    "    #``` dan densitas adalah rho. Densitas satuannya kg/m^3, jarak dalam km\n",
    "    \n",
    "    rx = xp - xq # jarak pengamat dan bola di sumbu x\n",
    "    ry = yp - yq # jarak pengamat dan bola di sumbu y\n",
    "    rz = zp - zq# jarak pengamat dan bola di sumbu z\n",
    "    \n",
    "    # menghitung jarak menggunakan persamaan Pythagoras\n",
    "    r = math.sqrt(math.pow(rx,2)+math.pow(ry,2)+math.pow(rz,2))\n",
    "    \n",
    "    # apabila jarak=0 maka dibuat fungsi error\n",
    "    if r == 0:\n",
    "        print(\"R-nya nol, ubah kedalaman!\")\n",
    "    \n",
    "    #jarak pangkat 3\n",
    "    r3 = math.pow(r,3)\n",
    "    \n",
    "    # menghitung total mass\n",
    "    tmass = 4.0 * pi * rho * (math.pow(a,2))/3\n",
    "    \n",
    "    # menghitung nilai percepatan gravitasi pada masing-masing komponen\n",
    "    gx = -gamma * tmass * rx/r3\n",
    "    gy = -gamma * tmass * ry/r3\n",
    "    gz = -gamma * tmass * rz/r3\n",
    "    \n",
    "    # konversi satuan\n",
    "    gx = gx * si2mg * km2m\n",
    "    gy = gy * si2mg * km2m\n",
    "    gz = gz * si2mg * km2m\n",
    "    \n",
    "    return gx, gy, gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selanjutnya kita gunakan looping untuk menghitung nilai `g`. Sebelumnya kita membuat persebaran data pengukuran di pengamat atau membuat grid seperti tadi, menggunakan:\n",
    "\n",
    "```python\n",
    "x = np.linspace(-50,50,100)\n",
    "y = np.linspace(-50,50,100)\n",
    "X,Y = np.meshgrid(x,y)\n",
    "```\n",
    "\n",
    "Pada fungsi di atas kita membuat persebaran koordinat teratur dari nilai -50 sampai 50 dengan jumlah data 100. Oleh karena itu maka apabila dibuat grid menggunakan `meshgrid` maka ukuran `X`, `Y` adalah 100x100.\n",
    "\n",
    "Setelah itu kita membuat array yang berisi 0 dengan ukuran 100x100 yang nanti akan kita isi dengan nilai `gz` menggunakan:\n",
    "\n",
    "```python\n",
    "G=np.zeros((100,100))\n",
    "```\n",
    "\n",
    "Langkah terakhir adalah melakukan `looping` dari 0-100 pada masing-masing indeks `i` dan `j` untuk menghitung nilai `G`. Ini dicermati pelan-pelan gapapa hehe :D. Nah nilai gz yang kemudian kita masukkan di matriks `G` yang tadi semula berisi 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(-50,50,100)\n",
    "y = np.linspace(-50,50,100)\n",
    "X,Y = np.meshgrid(x,y)\n",
    "G=np.zeros((100,100))\n",
    "for i in range(100):\n",
    "    for j in range(100):\n",
    "        gx1, gy1, gz1 = bola(20,-20,5,1,2600,X[i,j],Y[i,j],-2)\n",
    "        G[i,j]=gz1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selanjutnya kita plot yuk :D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "fig = plt.figure()\n",
    "ax = plt.axes(projection='3d')\n",
    "surf = ax.plot_surface(X, Y, G, cmap=cm.coolwarm,linewidth=0, antialiased=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Waah kerem, eit untuk praktikum ini kita pakai plot 2D dulu saja pakai kontur :D, seperti di bawah ini:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": [
    "plt.contourf(X,Y,G, cmap=cm.coolwarm)\n",
    "plt.title(\"Peta Medan Gravitasi karena Bola (zq=5, a=1, rho=3600, zp=-2)\")\n",
    "plt.ylabel(\"Northing\")\n",
    "plt.xlabel(\"Easting\")\n",
    "plt.show()"
   ]
  },
  {
   "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-5</p>\n",
    "    <h1>Tugas 5: Array dan Gridding Sederhana 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: 5</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: 5-[NIM]-Nama, contoh: 5-362705-Anang Sahroni</li>\n",
    "    </ul>\n",
    "</div>\n",
    "<div style=\"background-color: #dc3545; color: white \">\n",
    "    <ul>\n",
    "        <li>Deadline: Minggu, 26 April 2020 17.00 WIB</li>\n",
    "    </ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Buatlah peta gravitasi seperti di atas tetapi terdapat dua bola kemudian plotlah dalam peta kontur. Setelah itu buatlah sayatan seperti pada gambar di bawah ini. Berilah judul grafik/plot dan kelengkapan grafik yang lainnya.\n",
    "\n",
    "![tugas5](./material/tugas5.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}