dynamic_time_warping_cs_is_clay_more_peat_layers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6ce54fa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>jQuery(function() {if (jQuery(\"body.notebook_app\").length == 0) { jQuery(\".input_area\").toggle(); jQuery(\".prompt\").toggle();}});</script>"
      ]
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     "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": "e728ff94",
   "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": "code",
   "execution_count": 3,
   "id": "2aabae0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importing the dtw module. When using in academic works please cite:\n",
      "  T. Giorgino. Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package.\n",
      "  J. Stat. Soft., doi:10.18637/jss.v031.i07.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import pickle\n",
    "\n",
    "from dtw import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ff2950da",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Form template of layers\n",
    "# peat | clay | sand\n",
    "\n",
    "#Number of kinds of materials\n",
    "N = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "47570b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Layer names\n",
    "layer_names = ['peat', 'clay', 'sand']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "43286a08",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Templates with 1 layer\n",
    "templates_1 = []\n",
    "\n",
    "#Peat\n",
    "t = [1, 0, 0]\n",
    "templates_1.append(t)\n",
    "\n",
    "#Clay\n",
    "t = [0, 1, 0]\n",
    "templates_1.append(t)\n",
    "\n",
    "#Clayey sand\n",
    "#t = [0, 0, 1, 0]\n",
    "#templates_1.append(t)\n",
    "\n",
    "#Sand\n",
    "t = [0, 0, 1]\n",
    "templates_1.append(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "40d1812f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Templates with 1 layer\n",
    "templates_1 = []\n",
    "\n",
    "for i in range(N):\n",
    "    t = [0, 0, 0]\n",
    "    t[i] = 1\n",
    "    templates_1.append([t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a6a3e2e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#templates_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f0d903ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Templates with 2 layers\n",
    "templates_2 = []\n",
    "\n",
    "for i in range(N):\n",
    "    t1 = [0, 0, 0]\n",
    "    t1[i] = 1\n",
    "    for j in range(N):\n",
    "        if j!=i:\n",
    "            t2 = [0, 0, 0]\n",
    "            t2[j] = 1\n",
    "            templates_2.append([t1, t2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "70654765",
   "metadata": {},
   "outputs": [],
   "source": [
    "#templates_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0fd60d9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Templates with 3 layers\n",
    "templates_3 = []\n",
    "\n",
    "for i in range(N):\n",
    "    t1 = [0, 0, 0]\n",
    "    t1[i] = 1\n",
    "    for j in range(N):\n",
    "        if j!=i:\n",
    "            t2 = [0, 0, 0]\n",
    "            t2[j] = 1\n",
    "            for k in range(N):\n",
    "                if k!=j:\n",
    "                    t3 = [0, 0, 0]\n",
    "                    t3[k] = 1   \n",
    "                    templates_3.append([t1, t2, t3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc5bc91b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#templates_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4dbaef78",
   "metadata": {},
   "outputs": [],
   "source": [
    "templates = templates_1 + templates_2 + templates_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "19dd87f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add additional layers containing peat\n",
    "tp = [1, 0, 0] #Peat\n",
    "tc = [0, 1, 0] #Clay\n",
    "ts = [0, 0, 1] #Sand\n",
    "\n",
    "t = [tc, ts] #Layers other than peat\n",
    "\n",
    "#Length 4 layers\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        templates.append([tp, t[i], tp, t[j]])\n",
    "        \n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        templates.append([t[i], tp, t[j], tp]) \n",
    "        \n",
    "#Length 5 layers\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        templates.append([tp, t[i], tp, t[j], tp])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "952abe0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Weight of each template. Set equal to length of template\n",
    "weight_templates = [len(t) for t in templates]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "251093cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_templates = []\n",
    "for t in templates:\n",
    "    t1 = []\n",
    "    for item in t:\n",
    "        if item==[1,0,0]:\n",
    "            t1.append('peat')\n",
    "        if item==[0,1,0]:\n",
    "            t1.append('clay')  \n",
    "        if item==[0,0,1]:\n",
    "            t1.append('sand')\n",
    "    layer_templates.append(' - '.join(t1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "51332293",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['peat',\n",
       " 'clay',\n",
       " 'sand',\n",
       " 'peat - clay',\n",
       " 'peat - sand',\n",
       " 'clay - peat',\n",
       " 'clay - sand',\n",
       " 'sand - peat',\n",
       " 'sand - clay',\n",
       " 'peat - clay - peat',\n",
       " 'peat - clay - sand',\n",
       " 'peat - sand - peat',\n",
       " 'peat - sand - clay',\n",
       " 'clay - peat - clay',\n",
       " 'clay - peat - sand',\n",
       " 'clay - sand - peat',\n",
       " 'clay - sand - clay',\n",
       " 'sand - peat - clay',\n",
       " 'sand - peat - sand',\n",
       " 'sand - clay - peat',\n",
       " 'sand - clay - sand',\n",
       " 'peat - clay - peat - clay',\n",
       " 'peat - clay - peat - sand',\n",
       " 'peat - sand - peat - clay',\n",
       " 'peat - sand - peat - sand',\n",
       " 'clay - peat - clay - peat',\n",
       " 'clay - peat - sand - peat',\n",
       " 'sand - peat - clay - peat',\n",
       " 'sand - peat - sand - peat',\n",
       " 'peat - clay - peat - clay - peat',\n",
       " 'peat - clay - peat - sand - peat',\n",
       " 'peat - sand - peat - clay - peat',\n",
       " 'peat - sand - peat - sand - peat']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_templates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e89d067e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(layer_templates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ef484848",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Implementation of the DTW algorithm doesn't accept templates with just 1 element, so duplicating the first 4 elements\n",
    "\n",
    "for i in range(3):\n",
    "    templates[i] = [templates[i][0], templates[i][0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15e65e57",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "13de03bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_templates = len(templates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3c2793f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read from datafile\n",
    "datafile = '../Data/flevoland.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c1af3337",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_orig = pd.read_csv(datafile,skiprows=1,usecols=[1,2,3,4,5,6,7,8],names=['x','y','z','peat','clay_original','clayey_sand','sand','peat_on_PL'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "67059108",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fill absent data with zeroes\n",
    "df_orig.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6f8ac994",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set clayey sand to clay, for sake of simplicity, for reducing the number of clusters\n",
    "df_orig['clay'] = df_orig['clay_original'] + df_orig['clayey_sand']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "92a8a179",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Unique number of points in x, y, z\n",
    "N_x_unique = df_orig['x'].nunique()\n",
    "N_y_unique = df_orig['y'].nunique()\n",
    "N_z_unique = df_orig['z'].nunique()\n",
    "\n",
    "#Sort x, y, z values\n",
    "x_sorted = sorted(df_orig['x'].unique())\n",
    "y_sorted = sorted(df_orig['y'].unique())\n",
    "z_sorted = sorted(df_orig['z'].unique())\n",
    "\n",
    "#Minimum x, y, z values\n",
    "x_min = np.min(df_orig['x'])\n",
    "y_min = np.min(df_orig['y'])\n",
    "z_min = np.min(df_orig['z'])\n",
    "\n",
    "#Maximum x, y, z values\n",
    "x_max = np.max(df_orig['x'])\n",
    "y_max = np.max(df_orig['y'])\n",
    "z_max = np.max(df_orig['z'])\n",
    "\n",
    "#Step sizes in x, y, z\n",
    "x_step = x_sorted[1] - x_sorted[0]\n",
    "y_step = y_sorted[1] - y_sorted[0]\n",
    "z_step = z_sorted[1] - z_sorted[0]\n",
    "\n",
    "#Number of points in x, y, z. Have checked that all points in x, y, z are equidistant, i.e., no missing points.\n",
    "N_x = N_x_unique\n",
    "N_y = N_y_unique\n",
    "N_z = N_z_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a6889e7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create array of x, y, z indices \n",
    "indices_xyz = np.array([(df_orig['x']-x_min)/x_step, (df_orig['y']-y_min)/y_step, (df_orig['z']-z_min)/z_step])\n",
    "\n",
    "indices_xyz = indices_xyz.astype(int)\n",
    "\n",
    "#Indices in dataframe df below\n",
    "indices_df = np.ravel_multi_index(indices_xyz, (N_x, N_y, N_z))\n",
    "\n",
    "#Create meshgrid of x, y, z values\n",
    "xv, yv, zv = np.meshgrid(x_sorted, y_sorted, z_sorted, indexing='ij')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d31bc735",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create dataframe with a row for each x, y, z combination, and empty columns for the probabilities\n",
    "df = pd.DataFrame(np.array([xv.flatten(), yv.flatten(), zv.flatten(), np.full(N_x*N_y*N_z, -1), np.full(N_x*N_y*N_z, -1), np.full(N_x*N_y*N_z, -1)]).T, columns = ['x','y','z','peat','clay','sand'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "73213322",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['peat'][indices_df] = df_orig['peat']\n",
    "df['clay'][indices_df] = df_orig['clay']\n",
    "df['sand'][indices_df] = df_orig['sand']\n",
    "\n",
    "dataframe = df[['peat','clay','sand']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "478c0fd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arrange voxels into vertical stacks\n",
    "def stack_xy_probs(dataframe, N_x, N_y, N_z):\n",
    "\n",
    "    dimension_cube = [N_x, N_y, N_z]\n",
    "    num_rows_dataframe = len(dataframe)    \n",
    "    indices_dataframe = np.arange(0, num_rows_dataframe)\n",
    "    \n",
    "    indices_unraveled = np.unravel_index(indices_dataframe, dimension_cube)\n",
    "    \n",
    "    num_variables = len(dataframe.columns)    \n",
    "        \n",
    "    data_cube = np.zeros((N_x,N_y,N_z,num_variables))\n",
    "  \n",
    "    for i in range(num_variables):\n",
    "        data_i = np.zeros((N_x, N_y, N_z))\n",
    "        data_i[indices_unraveled[0], indices_unraveled[1], indices_unraveled[2]] = dataframe[dataframe.columns[i]]\n",
    "        data_cube[:,:,:,i] = data_i\n",
    "        \n",
    "    #List of data stacks\n",
    "    data_stacks = []\n",
    "    #x-position indices of data stacks\n",
    "    data_stacks_nx = []\n",
    "    #y-position indices of data stacks\n",
    "    data_stacks_ny = []    \n",
    "    \n",
    "    for ix in range(N_x):\n",
    "        for iy in range(N_y):\n",
    "            data_stack_z = data_cube[ix,iy].tolist()\n",
    "            data_stack = [item for item in data_stack_z if item != [-1,-1,-1] and item!=[0,0,0]]\n",
    "            if data_stack:\n",
    "                if len(data_stack)==1:\n",
    "                    data_stack = [data_stack[0], data_stack[0]]\n",
    "                data_stacks.append(data_stack)\n",
    "                data_stacks_nx.append(ix)\n",
    "                data_stacks_ny.append(iy)\n",
    "    return data_stacks, data_stacks_nx, data_stacks_ny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fc4b0527",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_stacks, data_stacks_nx, data_stacks_ny = stack_xy_probs(dataframe, N_x, N_y, N_z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7f412268",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment to run DTW algorithm\n",
    "\n",
    "# #data_stack_clusters will be filled with the cluster ids of the cluster the corresponding stack in data_stacks\n",
    "# data_stack_clusters = []\n",
    "\n",
    "# import time\n",
    "\n",
    "# start_time = time.time()\n",
    "\n",
    "# for index, query in enumerate(data_stacks):\n",
    "#    #distances contains the distance of the element \"query\" of data_stacks to every template in templates \n",
    "#    distances = []\n",
    "#    for template in templates:\n",
    "#        alignment = dtw(query, template, step_pattern='asymmetric', keep_internals=True, open_end=True, open_begin=True)\n",
    "#        distances.append(alignment.distance)\n",
    "#    min_distance = min(distances)\n",
    "#    #find the cluster id of the cluster template which has the mimimum distance to this stack\n",
    "#    cluster = distances.index(min_distance)\n",
    "#    data_stack_clusters.append(cluster)\n",
    "    \n",
    "# end_time = time.time()\n",
    "\n",
    "# print('Run time: %f min'%((end_time-start_time)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e25870a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open('data_stack_clusters_3_layers_cs_is_clay_more_peat_layers.p', 'wb') as outfile:\n",
    "#    pickle.dump(data_stack_clusters, outfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "95656140",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data_stack_clusters_3_layers_cs_is_clay_more_peat_layers.p', 'rb') as infile:\n",
    "   data_stack_clusters = pickle.load(infile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "56767eef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map with cluster ids\n",
    "cluster_map = -np.ones((N_y,N_x))\n",
    "\n",
    "#2-dimensional map is populated with cluster ids corresponding to each (x,y) point\n",
    "for i in range(len(data_stacks)):\n",
    "    cluster_map[data_stacks_ny[i], data_stacks_nx[i]] = data_stack_clusters[i]\n",
    "    \n",
    "cluster_map = cluster_map.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cc8f3a8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#with open('cluster_map_cs_is_clay.p', 'wb') as outfile:\n",
    "#       pickle.dump(cluster_map, outfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0c877b03",
   "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(cluster_map,cmap='rainbow')\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f785f781",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "most_common_cluster_tuples = Counter(data_stack_clusters).most_common(num_templates)\n",
    "most_common_clusters = [cluster[0] for cluster in most_common_cluster_tuples]\n",
    "most_common_layer_templates = [layer_templates[i] for i in most_common_clusters]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "72e70b2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(3, 8693),\n",
       " (13, 5726),\n",
       " (1, 5045),\n",
       " (21, 2162),\n",
       " (2, 754),\n",
       " (8, 752),\n",
       " (17, 320),\n",
       " (20, 290),\n",
       " (6, 289),\n",
       " (25, 267),\n",
       " (16, 254),\n",
       " (9, 246),\n",
       " (12, 184),\n",
       " (5, 161),\n",
       " (10, 136),\n",
       " (0, 83),\n",
       " (29, 57),\n",
       " (4, 49),\n",
       " (23, 35),\n",
       " (18, 23),\n",
       " (7, 19),\n",
       " (19, 19),\n",
       " (14, 15),\n",
       " (31, 11),\n",
       " (27, 8),\n",
       " (22, 5),\n",
       " (15, 4),\n",
       " (30, 4),\n",
       " (24, 2),\n",
       " (11, 2)]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_common_cluster_tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "77468a19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['peat - clay',\n",
       " 'clay - peat - clay',\n",
       " 'clay',\n",
       " 'peat - clay - peat - clay',\n",
       " 'sand',\n",
       " 'sand - clay',\n",
       " 'sand - peat - clay',\n",
       " 'sand - clay - sand',\n",
       " 'clay - sand',\n",
       " 'clay - peat - clay - peat',\n",
       " 'clay - sand - clay',\n",
       " 'peat - clay - peat',\n",
       " 'peat - sand - clay',\n",
       " 'clay - peat',\n",
       " 'peat - clay - sand',\n",
       " 'peat',\n",
       " 'peat - clay - peat - clay - peat',\n",
       " 'peat - sand',\n",
       " 'peat - sand - peat - clay',\n",
       " 'sand - peat - sand',\n",
       " 'sand - peat',\n",
       " 'sand - clay - peat',\n",
       " 'clay - peat - sand',\n",
       " 'peat - sand - peat - clay - peat',\n",
       " 'sand - peat - clay - peat',\n",
       " 'peat - clay - peat - sand',\n",
       " 'clay - sand - peat',\n",
       " 'peat - clay - peat - sand - peat',\n",
       " 'peat - sand - peat - sand',\n",
       " 'peat - sand - peat']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_common_layer_templates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fdff4651",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(most_common_layer_templates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1a35c08f",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_per_map = 6\n",
    "n_maps = int(np.ceil(len(templates)/clusters_per_map))\n",
    "\n",
    "#Set of n_maps maps, each containing cluster ids of clusters_per_map clusters at a time, in order of cluster size\n",
    "cluster_maps = []\n",
    "#cluster_ids contains sets of cluster ids, clusters_per_map at a time, in order of cluster size \n",
    "cluster_ids = []\n",
    "#cluster_templates contains the templates corresponding to cluster_ids\n",
    "cluster_templates = []\n",
    "\n",
    "#Initialize count of clusters in cluster_maps. It increases by clusters_per_map every iteration.\n",
    "count = 0\n",
    "\n",
    "for n in range(n_maps):\n",
    "    # cluster_ids_n contains the cluster ids of the clusters, in order of size, \n",
    "    # clusters_per_map number of clusters at a time\n",
    "    cluster_ids_n = most_common_clusters[count : min(count+clusters_per_map,len(templates))]\n",
    "    # cluster_templates_n contains the layer templates corresponding to cluster_ids_n\n",
    "    cluster_templates_n = most_common_layer_templates[count : min(count+clusters_per_map,len(templates))]\n",
    "    # Map of clusters, in which every (x,y) point will be filled in with the cluster id of the cluster it corresponds to\n",
    "    cluster_map = -np.ones((N_y,N_x))\n",
    "\n",
    "    for i in range(len(data_stack_clusters)):\n",
    "        if data_stack_clusters[i] in cluster_ids_n:\n",
    "            cluster_map[data_stacks_ny[i], data_stacks_nx[i]] = data_stack_clusters[i]\n",
    "    \n",
    "    cluster_maps.append(cluster_map)\n",
    "    cluster_ids.append(cluster_ids_n)\n",
    "    cluster_templates.append(cluster_templates_n)\n",
    "    \n",
    "    count = count + clusters_per_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "86f04a5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n=0\n",
    "\n",
    "#The nth cluster \"submap\", containing clusters_per_map clusters\n",
    "cluster_map_reference = cluster_maps[n]\n",
    "#Map to be filled with indices from 1 to clusters_map, corresponding to clusters in cluster_map_reference\n",
    "cluster_map = -np.ones((N_y,N_x))\n",
    "\n",
    "#cluster ids of clusters_per_map clusters\n",
    "cluster_id = cluster_ids[n]\n",
    "#cluster templates of clusters_per_map clusters\n",
    "cluster_template = cluster_templates[n]\n",
    "\n",
    "for index, item in enumerate(cluster_id):\n",
    "    #cluster_map is filled with integers from 1 to clusters_per_map, corresponding to the clusters in cluster_map_reference\n",
    "    cluster_map[cluster_map_reference==item] = index\n",
    "\n",
    "#Append an element \"sea\" to the cluster_template. This corresponds to all elements of cluster_map \n",
    "#which are not assigned to any cluster and have the value 0.\n",
    "cluster_template.insert(0,'sea')\n",
    "\n",
    "vmin = -1\n",
    "vmax = clusters_per_map\n",
    "xticklabels = 20\n",
    "yticklabels = 20\n",
    "cmap = 'tab10'\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5,5))\n",
    "ax = sns.heatmap(cluster_map, vmin=vmin-0.5, vmax=vmax-0.5, xticklabels=xticklabels, yticklabels=yticklabels, \n",
    "                 cmap=plt.cm.get_cmap(cmap, len(cluster_id)+1), cbar_kws={\"orientation\": \"horizontal\", 'fraction':0.045})\n",
    "ax.set_aspect('equal')\n",
    "c_bar = ax.collections[0].colorbar\n",
    "tick_locations = [vmin+i for i in range(vmax-vmin+1)]\n",
    "c_bar.set_ticks(list(tick_locations))\n",
    "c_bar.set_ticklabels(cluster_template)\n",
    "c_bar.ax.tick_params(rotation=90)\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "59bc259f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n=1\n",
    "\n",
    "cluster_map = -np.ones((N_y,N_x))\n",
    "cluster_map_reference = cluster_maps[n]\n",
    "\n",
    "cluster_id = cluster_ids[n]\n",
    "cluster_template = cluster_templates[n]\n",
    "\n",
    "for index, item in enumerate(cluster_id):\n",
    "    cluster_map[cluster_map_reference==item] = index\n",
    "\n",
    "cluster_template.insert(0,'sea')\n",
    "\n",
    "vmin = -1\n",
    "vmax = clusters_per_map\n",
    "xticklabels = 20\n",
    "yticklabels = 20\n",
    "cmap = 'tab10'\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5,5))\n",
    "ax = sns.heatmap(cluster_map, vmin=vmin-0.5, vmax=vmax-0.5, xticklabels=xticklabels, yticklabels=yticklabels, \n",
    "                 cmap=plt.cm.get_cmap(cmap, len(cluster_id)+1), cbar_kws={\"orientation\": \"horizontal\", 'fraction':0.045})\n",
    "ax.set_aspect('equal')\n",
    "c_bar = ax.collections[0].colorbar\n",
    "tick_locations = [vmin+i for i in range(vmax-vmin+1)]\n",
    "c_bar.set_ticks(list(tick_locations))\n",
    "c_bar.set_ticklabels(cluster_template)\n",
    "c_bar.ax.tick_params(rotation=90)\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "09134dda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n=2\n",
    "\n",
    "cluster_map = -np.ones((N_y,N_x))\n",
    "cluster_map_reference = cluster_maps[n]\n",
    "\n",
    "cluster_id = cluster_ids[n]\n",
    "cluster_template = cluster_templates[n]\n",
    "\n",
    "for index, item in enumerate(cluster_id):\n",
    "    cluster_map[cluster_map_reference==item] = index\n",
    "\n",
    "cluster_template.insert(0,'sea')\n",
    "\n",
    "vmin = -1\n",
    "vmax = clusters_per_map\n",
    "xticklabels = 20\n",
    "yticklabels = 20\n",
    "cmap = 'tab10'\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5,5))\n",
    "ax = sns.heatmap(cluster_map, vmin=vmin-0.5, vmax=vmax-0.5, xticklabels=xticklabels, yticklabels=yticklabels, \n",
    "                 cmap=plt.cm.get_cmap(cmap, len(cluster_id)+1), cbar_kws={\"orientation\": \"horizontal\", 'fraction':0.045})\n",
    "ax.set_aspect('equal')\n",
    "c_bar = ax.collections[0].colorbar\n",
    "tick_locations = [vmin+i for i in range(vmax-vmin+1)]\n",
    "c_bar.set_ticks(list(tick_locations))\n",
    "c_bar.set_ticklabels(cluster_template)\n",
    "c_bar.ax.tick_params(rotation=90)\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f1c87715",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n=3\n",
    "\n",
    "cluster_map = -np.ones((N_y,N_x))\n",
    "cluster_map_reference = cluster_maps[n]\n",
    "\n",
    "cluster_id = cluster_ids[n]\n",
    "cluster_template = cluster_templates[n]\n",
    "\n",
    "for index, item in enumerate(cluster_id):\n",
    "    cluster_map[cluster_map_reference==item] = index\n",
    "\n",
    "cluster_template.insert(0,'sea')\n",
    "\n",
    "vmin = -1\n",
    "vmax = clusters_per_map\n",
    "xticklabels = 20\n",
    "yticklabels = 20\n",
    "cmap = 'tab10'\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5,5))\n",
    "ax = sns.heatmap(cluster_map, vmin=vmin-0.5, vmax=vmax-0.5, xticklabels=xticklabels, yticklabels=yticklabels,\n",
    "                 cmap=plt.cm.get_cmap(cmap, len(cluster_id)+1), cbar_kws={\"orientation\": \"horizontal\", 'fraction':0.045})\n",
    "ax.set_aspect('equal')\n",
    "c_bar = ax.collections[0].colorbar\n",
    "tick_locations = [vmin+i for i in range(vmax-vmin+1)]\n",
    "c_bar.set_ticks(list(tick_locations))\n",
    "c_bar.set_ticklabels(cluster_template)\n",
    "c_bar.ax.tick_params(rotation=90)\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a33281ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n=4\n",
    "\n",
    "cluster_map = -np.ones((N_y,N_x))\n",
    "cluster_map_reference = cluster_maps[n]\n",
    "\n",
    "cluster_id = cluster_ids[n]\n",
    "cluster_template = cluster_templates[n]\n",
    "\n",
    "for index, item in enumerate(cluster_id):\n",
    "    cluster_map[cluster_map_reference==item] = index\n",
    "\n",
    "cluster_template.insert(0,'sea')\n",
    "\n",
    "vmin = -1\n",
    "vmax = clusters_per_map\n",
    "xticklabels = 20\n",
    "yticklabels = 20\n",
    "cmap = 'tab10'\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5,5))\n",
    "ax = sns.heatmap(cluster_map, vmin=vmin-0.5, vmax=vmax-0.5, xticklabels=xticklabels, yticklabels=yticklabels, \n",
    "                 cmap=plt.cm.get_cmap(cmap, len(cluster_id)+1), cbar_kws={\"orientation\": \"horizontal\", 'fraction':0.045})\n",
    "ax.set_aspect('equal')\n",
    "c_bar = ax.collections[0].colorbar\n",
    "tick_locations = [vmin+i for i in range(vmax-vmin+1)]\n",
    "c_bar.set_ticks(list(tick_locations))\n",
    "c_bar.set_ticklabels(cluster_template)\n",
    "c_bar.ax.tick_params(rotation=90)\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "67034c1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'x':data_stacks_nx, 'y': data_stacks_ny, 'stack': data_stacks}\n",
    "df = pd.DataFrame(data=d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "19f92314",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n=0\n",
    "\n",
    "cluster_map = -np.ones((N_y,N_x))\n",
    "cluster_map_reference = cluster_maps[n]\n",
    "\n",
    "cluster_id = cluster_ids[n]\n",
    "cluster_template = cluster_templates[n]\n",
    "\n",
    "for index, item in enumerate(cluster_id):\n",
    "    cluster_map[cluster_map_reference==item] = index\n",
    "\n",
    "vmin = -1\n",
    "vmax = clusters_per_map\n",
    "xticklabels = 10\n",
    "yticklabels = 10\n",
    "cmap = 'tab10'\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,9))\n",
    "ax = sns.heatmap(cluster_map, vmin=vmin-0.5, vmax=vmax-0.5, xticklabels=xticklabels, yticklabels=yticklabels, \n",
    "                 cmap=plt.cm.get_cmap(cmap, len(cluster_id)+1), cbar_kws={\"orientation\": \"horizontal\", 'fraction':0.045})\n",
    "c_bar = ax.collections[0].colorbar\n",
    "tick_locations = [vmin+i for i in range(vmax-vmin+1)]\n",
    "c_bar.set_ticks(list(tick_locations))\n",
    "c_bar.set_ticklabels(cluster_template)\n",
    "c_bar.ax.tick_params(rotation=90)\n",
    "ax.grid()\n",
    "ax.invert_yaxis()"
   ]
  }
 ],
 "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
}