{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8a6abcb5",
   "metadata": {},
   "source": [
    "# Plot a geological map\n",
    "O. Kaufmann, 2021."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "60b69616",
   "metadata": {},
   "outputs": [],
   "source": [
    "from geometron.plot import geological_maps as gm\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.path import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "538a3645-d675-4685-b1af-4dcabd5e11be",
   "metadata": {},
   "source": [
    "## Create a polygon_dict (could be the output of gempy.section_utils.get_polygon_dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fd61368f-bc07-42b9-ba88-fe01ad890147",
   "metadata": {},
   "outputs": [],
   "source": [
    "polygon_dict = {'basement': [Path(np.array([[45., 88.95628197], [47., 88.98989156], [47.17201595, 89.], [49., 89.10830724],\n",
    "                                            [51., 89.29874656], [53., 89.54807511], [55., 89.84381045], [57., 90.17447269],\n",
    "                                            [59., 90.52982628], [61., 90.90093079], [61.5214998, 91.], [63., 91.28253196],\n",
    "                                            [65., 91.66539587], [67., 92.04275786], [69., 92.40972527], [71., 92.76212334],\n",
    "                                            [72.42435886, 93.], [73., 93.09685556], [75., 93.41097931], [77., 93.70065013],\n",
    "                                            [79., 93.9638618], [81., 94.19891532], [83., 94.40433285], [85., 94.57878865],\n",
    "                                            [87., 94.72105937], [89., 94.82999068], [91., 94.90447585], [93., 94.9434423],\n",
    "                                            [95., 94.94584333], [97., 94.9106531], [99., 94.8368636], [99., 95.], [99., 97.],\n",
    "                                            [99., 99.], [97., 99.], [95., 99.], [93., 99.], [91., 99.], [89., 99.], [87., 99.],\n",
    "                                            [85., 99.], [83., 99.], [81., 99.], [79., 99.], [77., 99.], [75., 99.], [73., 99.],\n",
    "                                            [71., 99.], [69., 99.], [67., 99.], [65., 99.], [63., 99.], [61., 99.], [59., 99.],\n",
    "                                            [57., 99.], [55., 99.], [53., 99.], [51., 99.], [49., 99.], [47., 99.], [45., 99.],\n",
    "                                            [43., 99.], [41., 99.], [39., 99.], [37., 99.], [35., 99.], [33., 99.], [31., 99.],\n",
    "                                            [29., 99.], [27., 99.], [25., 99.], [23., 99.], [21., 99.], [19., 99.], [17., 99.],\n",
    "                                            [15., 99.], [13., 99.], [12.31551404, 99.], [13., 98.7009608], [15., 97.82318079],\n",
    "                                            [16.88129696, 97.], [17., 96.94909998], [19., 96.09187035], [21., 95.24443626],\n",
    "                                            [21.58494048, 95.], [23., 94.41960017], [25., 93.6186686], [26.59801749, 93.],\n",
    "                                            [27., 92.84707444], [29., 92.12106477], [31., 91.44076694], [32.41482327, 91.],\n",
    "                                            [33., 90.82079858], [35., 90.2776083], [37., 89.81598784], [39., 89.44722021],\n",
    "                                            [41., 89.17954694], [43., 89.01637388], [43.53528514, 89.], [45., 88.95628197]]),\n",
    "                                  np.array([ 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
    "                                            2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
    "                                            2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
    "                                            2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 79], dtype=np.uint8))],\n",
    "                'A3': [Path(np.array([[33., 82.7262336], [35., 82.22744216], [37., 81.80843753], [39., 81.48186824], [41., 81.25710252],\n",
    "                                      [43., 81.1374437], [45., 81.11927427], [47., 81.19368415], [49., 81.34884191], [51., 81.57173079],\n",
    "                                      [53., 81.84917143], [55., 82.16845566], [57., 82.517797], [59., 82.88664721], [59.59923552, 83.], \n",
    "                                      [61., 83.26455687], [63., 83.64316134], [65., 84.01627083], [67., 84.37835026], [69., 84.72458182],\n",
    "                                      [70.69114745, 85.], [71., 85.05021691], [73., 85.34908004], [75., 85.62016577], [77., 85.86089491],\n",
    "                                      [79., 86.06916203], [81., 86.24323426], [83., 86.38162662], [85., 86.48300335], [87., 86.54611682],\n",
    "                                      [89., 86.5697741], [91., 86.55282013], [93., 86.49412977], [95., 86.39260451], [97., 86.24717154],\n",
    "                                      [99., 86.05678404], [99., 87.], [99., 89.], [99., 91.], [99., 93.], [99., 94.8368636],\n",
    "                                      [97., 94.9106531], [95., 94.94584333], [93., 94.9434423], [91., 94.90447585], [89., 94.82999068],\n",
    "                                      [87., 94.72105937], [85., 94.57878865], [83., 94.40433285], [81., 94.19891532], [79., 93.9638618],\n",
    "                                      [77., 93.70065013], [75., 93.41097931], [73., 93.09685556], [72.42435886, 93.], [71., 92.76212334],\n",
    "                                      [69., 92.40972527], [67., 92.04275786], [65., 91.66539587], [63., 91.28253196], [61.5214998, 91.],\n",
    "                                      [61., 90.90093079], [59., 90.52982628], [57., 90.17447269], [55., 89.84381045], [53., 89.54807511],\n",
    "                                      [51., 89.29874656], [49., 89.10830724], [47.17201595, 89.], [47., 88.98989156], [45., 88.95628197],\n",
    "                                      [43.53528514, 89.], [43., 89.01637388], [41., 89.17954694], [39., 89.44722021], [37., 89.81598784],\n",
    "                                      [35., 90.2776083], [33., 90.82079858], [32.41482327, 91.], [31., 91.44076694], [29., 92.12106477],\n",
    "                                      [27., 92.84707444], [26.59801749, 93.], [25., 93.6186686], [23., 94.41960017], [21.58494048, 95.],\n",
    "                                      [21., 95.24443626], [19., 96.09187035], [17., 96.94909998], [16.88129696, 97.], [15., 97.82318079],\n",
    "                                      [13., 98.7009608], [12.31551404, 99.], [11., 99.], [ 9., 99.], [ 7., 99.], [ 5., 99.], [ 3., 99.],\n",
    "                                      [ 1., 99.], [ 1., 97.], [ 1., 95.], [ 1., 94.00732899], [ 3., 93.30103895], [ 3.83506107, 93.], \n",
    "                                      [ 5., 92.58641982], [ 7., 91.8655555], [ 9., 91.13990418], [ 9.37676004, 91.], [11., 90.40670303], \n",
    "                                      [13., 89.66996416], [14.80793005, 89.], [15., 88.92989913], [17., 88.1873885], [19., 87.44652134], \n",
    "                                      [20.2041995, 87.], [21., 86.70925357], [23., 85.9814006], [25., 85.26741626], [25.76598847, 85.], \n",
    "                                      [27., 84.5752538], [29., 83.91447514], [31., 83.29349271], [32.02768903, 83.], [33., 82.7262336]]), \n",
    "                            np.array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, \n",
    "                                      2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
    "                                      2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
    "                                      2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 79], dtype=np.uint8))],\n",
    "                'B': [Path(np.array([[27., 1.], [29., 1.], [31., 1.], [33., 1.], [35., 1.], [37., 1.], [39., 1.], [41., 1.], [43., 1.],\n",
    "                                     [45., 1.], [47., 1.], [49., 1.], [51., 1.], [53., 1.], [55., 1.], [57., 1.], [59., 1.], \n",
    "                                     [60.8520274, 1.], [60.2649941, 3.], [59.38635797, 5.], [59., 5.6577647], [58.21897221, 7.],\n",
    "                                     [57., 8.68418863], [56.77222485, 9.], [55.03770251, 11.], [55., 11.03746315], [53., 12.99965848],\n",
    "                                     [52.99964626, 13.], [51., 14.69474671], [50.62835032, 15.], [49., 16.21409689], [47.89562825, 17.],\n",
    "                                     [47., 17.60820825], [45., 18.9102079], [44.85670975, 19.], [43., 20.2205665], [41.84279481, 21.],\n",
    "                                     [41., 21.619346], [39.18693898, 23.], [39., 23.15476312], [37., 24.81833305], [36.77688291, 25.],\n",
    "                                     [35., 26.5594422], [34.48101586, 27.], [33., 28.32228855], [32.20323224, 29.], [31., 30.04536365],\n",
    "                                     [29.82935038, 31.], [29., 31.67038691], [27.21772252, 33.], [27., 33.15699637], [25., 34.42165973],\n",
    "                                     [23.95295441, 35.], [23., 35.49632552], [21., 36.33953218], [19., 36.98861498], [18.93765214, 37.],\n",
    "                                     [17., 37.32866833], [15., 37.38789188], [13., 37.09100846], [12.73564659, 37.], [11., 36.09702894],\n",
    "                                     [ 9.83183649, 35.], [ 9., 33.32820065], [ 8.88311206, 33.], [ 8.75600401, 31.], [ 9., 29.6347343],\n",
    "                                     [ 9.11522495, 29.], [ 9.87356233, 27.], [10.88161315, 25.], [11., 24.81280301], [12.13889947, 23.],\n",
    "                                     [13., 21.75224274], [13.50803336, 21.], [14.92451002, 19.], [15., 18.89862459], [16.34687265, 17.],\n",
    "                                     [17., 16.06479653], [17.70038902, 15.], [18.97305951, 13.], [19., 12.95854559], [20.19962587, 11.],\n",
    "                                     [21., 9.66731542], [21.38191521, 9.], [22.56506398, 7.], [23., 6.29690161], [23.78890683, 5.],\n",
    "                                     [25., 3.11389055], [25.07388008, 3.], [26.49069087, 1.], [27., 1.]]), \n",
    "                           np.array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
    "                                     2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
    "                                     2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 79], dtype=np.uint8)),\n",
    "                      Path(np.array([[37., 66.75009565], [39., 66.4960496], [41., 66.31691736], [43., 66.21680513], [45., 66.19711566],\n",
    "                                     [47., 66.25639151], [49., 66.39027131], [51., 66.59155428], [53., 66.85038906], [53.98832777, 67.],\n",
    "                                     [55., 67.14376338], [57., 67.45618233], [59., 67.78462642], [61., 68.11611163], [63., 68.43946354],\n",
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    "                                     [35., 82.22744216], [33., 82.7262336], [32.02768903, 83.], [31., 83.29349271], [29., 83.91447514],\n",
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    "                                     [13., 89.66996416], [11., 90.40670303], [ 9.37676004, 91.], [ 9., 91.13990418], [ 7., 91.8655555],\n",
    "                                     [ 5., 92.58641982], [ 3.83506107, 93.], [ 3., 93.30103895], [ 1., 94.00732899], [ 1., 93.],\n",
    "                                     [ 1., 91.], [ 1., 89.], [ 1., 87.], [ 1., 85.], [ 1., 83.], [ 1., 81.], [ 1., 79.], [ 1., 77.],\n",
    "                                     [ 1., 75.], [ 1., 73.], [ 1., 72.27291762], [ 3., 72.13161886], [ 5., 71.97806866], \n",
    "                                     [ 7., 71.80837326], [ 9., 71.61899906], [11., 71.40696138], [13., 71.16998884], [14.26671685, 71.],\n",
    "                                     [15., 70.89583558], [17., 70.57477449], [19., 70.2291934], [21., 69.86181498], [23., 69.47670003],\n",
    "                                     [25., 69.07938775], [25.38164042, 69.], [27., 68.64447826], [29., 68.21027139], [31., 67.79525318],\n",
    "                                     [33., 67.41011882], [35., 67.0657465], [35.4410497, 67.], [37., 66.75009565]]), \n",
    "      np.array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
    "                2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
    "                2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
    "                2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 79], dtype=np.uint8))],\n",
    "                'A': [Path(np.array([[ 3., 1.], [ 5., 1.], [ 7., 1.], [ 9., 1.], [11., 1.], [13., 1.], [15., 1.], [17., 1.], [19., 1.],\n",
    "                                     [21., 1.], [23., 1.], [25., 1.], [26.49069087, 1.], [25.07388008, 3.], [25., 3.11389055],\n",
    "                                     [23.78890683, 5.], [23., 6.29690161], [22.56506398, 7.], [21.38191521, 9.], [21., 9.66731542],\n",
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    "                                     [31., 30.04536365], [32.20323224, 29.], [33., 28.32228855], [34.48101586, 27.], [35., 26.5594422],\n",
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    "                                     [37., 66.75009565], [35.4410497, 67.], [35., 67.0657465], [33., 67.41011882], [31., 67.79525318],\n",
    "                                     [29., 68.21027139], [27., 68.64447826], [25.38164042, 69.], [25., 69.07938775], [23., 69.47670003],\n",
    "                                     [21., 69.86181498], [19., 70.2291934], [17., 70.57477449], [15., 70.89583558], [14.26671685, 71.],\n",
    "                                     [13., 71.16998884], [11., 71.40696138], [ 9., 71.61899906], [ 7., 71.80837326], [ 5., 71.97806866],\n",
    "                                     [ 3., 72.13161886], [ 1., 72.27291762], [ 1., 71.], [ 1., 69.], [ 1., 67.], [ 1., 65.], [ 1., 63.],\n",
    "                                     [ 1., 61.], [ 1., 59.], [ 1., 57.], [ 1., 55.], [ 1., 53.], [ 1., 51.], [ 1., 49.], [ 1., 47.],\n",
    "                                     [ 1., 45.], [ 1., 43.], [ 1., 41.], [ 1., 39.], [ 1., 37.], [ 1., 35.], [ 1., 33.], [ 1., 31.],\n",
    "                                     [ 1., 29.], [ 1., 27.], [ 1., 25.], [ 1., 23.], [ 1., 21.], [ 1., 19.], [ 1., 17.], [ 1., 15.],\n",
    "                                     [ 1., 13.], [ 1., 11.], [ 1., 9.], [ 1., 7.], [ 1., 5.], [ 1., 3.], [ 1., 1.], [ 3., 1.]]), \n",
    "            np.array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
    "                      2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
    "                      2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
    "                      2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
    "                      2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
    "                      2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
    "                      2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 79], dtype=np.uint8))]}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e305bd50-23f4-484a-8921-976377c530ab",
   "metadata": {},
   "source": [
    "## Set a color dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b4709402-3b65-49e2-a5fc-dbf0581f66d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "color_dict = {'basement': '#728f02', 'A3': '#ffbe00', 'B': '#9f0052', 'A': '#015482'}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2122fb1-d123-4a22-bfc3-398741d3df82",
   "metadata": {},
   "source": [
    "## Specify the map extent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0300eaec-942c-4006-a959-641439731ac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "extent=[0.0, 100.0, 0.0, 100.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22285085-7d8b-4a1b-a29b-18e6cdf4ba51",
   "metadata": {},
   "source": [
    "## Display the map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8c7aa394",
   "metadata": {
    "nbsphinx-thumbnail": {
     "tooltip": "This tooltip message was defined in cell metadata"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10,10))\n",
    "gm.plot_map(polygon_dict, color_dict, extent, ax=ax)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}