TwoLevelSOM_log_rho.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e50d75eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>jQuery(function() {if (jQuery(\"body.notebook_app\").length == 0) { jQuery(\".input_area\").toggle(); jQuery(\".prompt\").toggle();}});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<button onclick=\"jQuery('.input_area').toggle(); jQuery('.prompt').toggle();\">Toggle code</button>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "from IPython.display import HTML\n",
    "import IPython.core.display as di # Example: di.display_html('<h3>%s:</h3>' % str, raw=True)\n",
    "\n",
    "# This line will hide code by default when the notebook is exported as HTML\n",
    "di.display_html('<script>jQuery(function() {if (jQuery(\"body.notebook_app\").length == 0) { jQuery(\".input_area\").toggle(); jQuery(\".prompt\").toggle();}});</script>', raw=True)\n",
    "\n",
    "# This line will add a button to toggle visibility of code blocks, for use with the HTML export version\n",
    "di.display_html('''<button onclick=\"jQuery('.input_area').toggle(); jQuery('.prompt').toggle();\">Toggle code</button>''', raw=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d4017b89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
       "    return false;\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
    "    return false;\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7b58f32",
   "metadata": {},
   "source": [
    "# Step 1: Using SOM to identify low-resistivity regions\n",
    "# Step 2: Using SOM to identify spatially separated low-resistivity regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e060bd58",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from resistivity_functions import *\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "90658374",
   "metadata": {},
   "outputs": [],
   "source": [
    "datafile = '../Data/rho.txt'\n",
    "\n",
    "dataframe_original = pd.read_csv(datafile,skiprows=1,usecols=[1,2,3,4],names=['x','y','z','rho'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4ce44c96",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe_original = dataframe_original.replace(-9999.0,1e6)\n",
    "\n",
    "dataframe = np.log10(dataframe_original['rho'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "480be1ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('log_rho_binary_cluster.p', 'rb') as infile:\n",
    "    som = pickle.load(infile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fbf1c2aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = som.dataframe_to_numpy_array()\n",
    "masked_data = som.mask_data(data=data,mask_value=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dbd5570",
   "metadata": {},
   "source": [
    "## SOM 1: Identifying low-resistivity and high-resistivity regions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96030cb5",
   "metadata": {},
   "source": [
    "### U-matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7dee3878",
   "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": [
    "som.display_u_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5ea93be8",
   "metadata": {},
   "outputs": [],
   "source": [
    "som_x_dim = 50\n",
    "som_y_dim = 70\n",
    "\n",
    "vector_length = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0682349d",
   "metadata": {},
   "outputs": [],
   "source": [
    "som_weights = som.get_weights_normalized()\n",
    "\n",
    "som_weights_flattened = np.zeros((som_x_dim*som_y_dim, vector_length))\n",
    "\n",
    "for i in range(som_x_dim):\n",
    "    for j in range(som_y_dim):\n",
    "        som_weights_flattened[i*som_y_dim+j,:] = som_weights[i,j]\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "    \n",
    "clustering = KMeans(n_clusters=2,random_state=1).fit(som_weights_flattened)\n",
    "\n",
    "labels = clustering.labels_\n",
    "clusters = labels.reshape(som_x_dim,som_y_dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23d6112e",
   "metadata": {},
   "source": [
    "### Clustering SOM using K-Means clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "570e309d",
   "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": [
    "ax = sns.heatmap(clusters, square=True, cmap=\"YlGnBu\")\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d58f6f99",
   "metadata": {},
   "outputs": [],
   "source": [
    "normalized_data = som.normalize_data(masked_data)\n",
    "reshaped_data = normalized_data.reshape(-1,1)\n",
    "data_cluster = clustering.predict(reshaped_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "183744d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe_original['cluster_id'] = data_cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f9d9f1c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = Data(dataframe=dataframe_original)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe0bfec9",
   "metadata": {},
   "source": [
    "### Value: z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4b433657",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "11bb37b156004a36b5da898dc33052d9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=-12.0, description='value', max=28.0, min=-50.0, step=2.0), Output()),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.display_cross_section(variable='z',quantity='cluster_id',logscale=False, vmin=0, vmax=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3e7e1de",
   "metadata": {},
   "source": [
    "### Value: x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fff2d1a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b1972d1a1764b249e9e40c4b30db5ad",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=243050.0, description='value', max=245400.0, min=240700.0, step=50.0),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.display_cross_section(variable='x',quantity='cluster_id',logscale=False, vmin=0, vmax=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44563210",
   "metadata": {},
   "source": [
    "### Value: y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ed4a3a27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7b7f547a6f09461792980efb5fd58105",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=561250.0, description='value', max=565000.0, min=557500.0, step=50.0),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.display_cross_section(variable='y',quantity='cluster_id',logscale=False, vmin=0, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6e6c2f9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_cluster = -(data_cluster - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7a5842a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cluster_resistivity = data_cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d82d0230",
   "metadata": {},
   "outputs": [],
   "source": [
    "low_resistivity_indices = np.where(data_cluster==1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d40dfeb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "low_resistivity_indices = np.reshape(low_resistivity_indices,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "57be119e",
   "metadata": {},
   "outputs": [],
   "source": [
    "low_resistivity_indices = np.asarray(low_resistivity_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2c01477c",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_low_resistivity_indices = low_resistivity_indices.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9dac093d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_size = len(list_low_resistivity_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bfc55330",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6d88aed7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#np.size(data_cluster)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "638ac107",
   "metadata": {},
   "source": [
    "## SOM 2: Identifying spatially separated low-resistivity regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d17b1dd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe_coordinates = dataframe_original[['x','y','z']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9e9a6cb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe = dataframe_coordinates.iloc[list_low_resistivity_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7010c21a",
   "metadata": {},
   "outputs": [],
   "source": [
    "som_x_dim = 30\n",
    "som_y_dim = 50\n",
    "\n",
    "vector_length = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "16bef358",
   "metadata": {},
   "outputs": [],
   "source": [
    "som = SOM(dataframe, input_len=vector_length, som_x_dim=som_x_dim, som_y_dim=som_y_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ef969eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "som.train_som(max_iterations=data_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0f94d75",
   "metadata": {},
   "source": [
    "### U-matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9a669833",
   "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": [
    "som.display_u_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "17bd570b",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('som_low_resistivity_coordinates.p', 'wb') as outfile:\n",
    "    pickle.dump(som, outfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "54cb1f37",
   "metadata": {},
   "outputs": [],
   "source": [
    "som_weights = som.get_weights_normalized()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ed59a45e",
   "metadata": {},
   "outputs": [],
   "source": [
    "som_weights_flattened = np.zeros((som_x_dim*som_y_dim, vector_length))\n",
    "\n",
    "for i in range(som_x_dim):\n",
    "    for j in range(som_y_dim):\n",
    "        som_weights_flattened[i*som_y_dim+j,:] = som_weights[i,j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "eff44233",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "    \n",
    "clustering = KMeans(n_clusters=3,random_state=1).fit(som_weights_flattened)\n",
    "\n",
    "labels = clustering.labels_\n",
    "clusters = labels.reshape(som_x_dim,som_y_dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2587a3c",
   "metadata": {},
   "source": [
    "### Clustering SOM using K-Means clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "04cc7a8a",
   "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": [
    "ax = sns.heatmap(clusters, square=True, cmap=\"YlGnBu\")\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "72574f04",
   "metadata": {},
   "outputs": [],
   "source": [
    "#som.display_u_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0821a510",
   "metadata": {},
   "outputs": [],
   "source": [
    "normalized_data = som.normalize_data(dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "32038eb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cluster = clustering.predict(normalized_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "91155f81",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1ea71eb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cluster = data_cluster + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2735b415",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cluster_resistivity_position = np.zeros(len(data_cluster_resistivity),dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "92ca8c55",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cluster_resistivity_position[list_low_resistivity_indices] = data_cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c07ffb37",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_cluster_resistivity_position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8cc98362",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_clusters = np.max(data_cluster_resistivity_position)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "072c658b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe_original['cluster_id_resistivity_position'] = data_cluster_resistivity_position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "46bc12ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = Data(dataframe=dataframe_original)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6a3216e",
   "metadata": {},
   "source": [
    "### Value: z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4bb8a10c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a876fcc4678b4f5f8d9f811ab4316dae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=-12.0, description='value', max=28.0, min=-50.0, step=2.0), Output()),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.display_cross_section(variable='z',quantity='cluster_id_resistivity_position',logscale=False, vmin=0, vmax=num_clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1fa2c67",
   "metadata": {},
   "source": [
    "### Value: x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6a0b08bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d32ee60ede474a6a9112ec4724e0ce15",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=243050.0, description='value', max=245400.0, min=240700.0, step=50.0),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.display_cross_section(variable='x',quantity='cluster_id_resistivity_position',logscale=False, vmin=0, vmax=num_clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b52e4a81",
   "metadata": {},
   "source": [
    "### Value: y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "959935eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1c2ad69743d54336bd3d0afc1d5fbe4b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=561250.0, description='value', max=565000.0, min=557500.0, step=50.0),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.display_cross_section(variable='y',quantity='cluster_id_resistivity_position',logscale=False, vmin=0, vmax=num_clusters)"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}