{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Vestland database for ML\n", "\n", "Linn Alexandra Emhjellen. 2021" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import os\n", "import joblib\n", "import matplotlib.pyplot as plt\n", "from sklearn.preprocessing import OneHotEncoder\n", "\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# random pixels from Vestland with GIS-features. For validation at the end\n", "#df_random_Vestland = pd.read_excel('Vestland_pixels_2.xlsx')\n", "#df_random_Vestland = pd.read_excel('Lærdal_Aurland_500k_pixels2.xlsx')\n", "#df_Vestland_database = pd.read_excel('test_area_pixel_features.xlsx')\n", "#df_Vestland_database = pd.read_excel('Lærdal_gård_bø_fishnet20_label_features.xlsx')\n", "#df_Vestland_database = pd.read_excel('copy of Lærdal_Aurland_close_area_10_m_pixels_features.xlsx')\n", "\n", "#df_Vestland_database = pd.read_csv('lærdal_aurland_10m_pixels_close.csv')\n", "\n", "#df_Vestland_database = pd.read_csv('Vestland_West_features_10m.csv')\n", "df_Vestland_database = pd.read_csv('Bohme_features_20m.csv')\n", "df_Vestland_database = df_Vestland_database.dropna()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[array([108, 113, 143, 402, 423, 426, 432, 437, 440], dtype=int64)]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#encode bedrock values\n", "\n", "bedrock = df_Vestland_database[[\"Bedrock\"]]\n", "\n", "bedrock_encoder = OneHotEncoder()\n", "bedrock_encoded = bedrock_encoder.fit_transform(bedrock)\n", "\n", "bedrock = bedrock_encoded.toarray()\n", "bedrock_encoder.categories_" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# sorted same as array \n", "\n", "#bedrock_groups = ['Granit','Granodiorite','Monzonite','Monzodiorite', 'Gabbro','Pyroksenite','Charnockite','Anorthosite','Rhyolite','Phyllite','Calcareous phyllite','Metasandstone', 'Quartzite','Quartz schist','Mica gneiss','Amphibole gneiss','Granitic gneiss','Tonalitic gneiss','Monzonitic gneiss','Orthopyroxene gneiss','Banded gneiss','Amphibolite','Metagabbro']\n", "\n", "#bedrock_Vestland = ['Granite','Granodiorite','Tonalite','Trondhjemite','Syenite','Monzonite','Monzodiorite','Quartz diorite','Diorite','Gabbro','Norite','Peridotite','Pyroksenite','Charnockite','Mangerite','Anorthosite','Mafic dyke (Diabase, Dolerite)','Pegmatite/aplite','Felsic volcanic rock','Rhyolite','Dacite','Intermediate volcanic rock','Andesite','Mafic volcanic rock','Basalt',\n", " # 'Pyroclastic rock','Volcanic breccia','Siltstone','Sandstone','Greywacke','Arkose','Konglomerate','Sedimentary breccia','Limestone','Tuffite','Shale','Phyllite','Mica schist','Garnet mica schist','Calcareous phyllite','Calcareous mica schist','Amphibole schist','Graphitic schist','Calcite marble',\n", " #'Metasandstone','Metagreywacke','Meta-arkose','Quartzite','Quartz schist','Mica gneiss','Calc-silicate rock','Amphibole gneiss','Granitic gneiss','Granodioritic gneiss','Tonalitic gneiss','Quartz dioritic gneiss','Monzonitic gneiss','Dioritic gneis','Orthopyroxene gneiss','Migmatite','Augengneiss',\n", " #'Banded gneiss','Greenschist','Greenstone','Amphibolite','Metagabbro','Eclogite','Serpentinite','Mylonite/Phyllonite','Cataclasite']\n", "\n", "#bedrock_Vestland = ['Granite','Granodiorite','Monzonite','Monzodiorite','Quartx diorite','Gabbro','Pyroksenitt','Charnockitt','Anorthosite','Rhyolite','Phyllite','Calcareous phyllite','Metasandstone','Quartzite','Quartz schist','Mica gneiss','Amphibole gneiss','Granitic gneiss','Granodioritic gneiss','Tonalitic gneiss','Monzonitic gneiss','Orthopyroxene gneiss','Migmatite','Augengneiss','Banded gneiss','Amphibolite','Metagabbro','Mylonite/Phyllonite','Cataclasite']\n", "#bedrock_Vestland = ['Granite','Monzonite','Monzodiorite','Charnockite','Granitic gneiss','Tonalitic gneiss']\n", "#bedrock_Vestland = ['Granite','Granodiorite','Monzonite','Monzodiorite','Gabbro','Charnockite','Anorthosite','Phyllite','Quartzite','Granitic gneiss','Tonalitic gneiss','Orthopyroxene gneiss','Cataclasite']\n", "\n", "#bedrock_Vestland = ['Granit','Monzonite','Monzodiorite','Charnockite','Anorthosite','Granitic gneiss','Tonalitic gneiss']\n", "#bedrock_Vestland = ['Tonalite','Diorite','Gabbro','Anorthosite','Rhyolite','Dacite','Sandstone','Arkose','Konglomerate','Phyllite',\n", " # 'Mica schist','Quartzite','Quartz schist','Granitic gneiss','Granodioritic gneiss','Tonalitic gneiss','Migmatite','Banded gneiss','Greenschist',\n", " #'Greenstone','Amphibolite','Metagabbro','Serpentinite']\n", " \n", "#bedrock_Vestland = ['Dacite','Sandstone','Konglomerate','Mica schist','Quartz schist','Granitic gneiss','Tonalitic gneiss','Banded gneiss','Amphibolite']\n", "bedrock_Vestland = ['Monzonite','Gabbro','Anorthosite','Phyllite','Tonalitic gneiss','Mica gneiss','Tonalitic gneiss','Orthopyroxene gneiss','Migmatite']\n", "df_bedrock = pd.DataFrame(bedrock, columns=bedrock_Vestland)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# merging the one-hot-encoded bedrock dataframe to the other parameters\n", "df_Vestland_database = df_Vestland_database.reset_index() #Need to do this, don't know why indexes was changed.\n", "df_Vestland_database = pd.concat([df_Vestland_database, df_bedrock], axis=1)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "aspect_categorical = []\n", "\n", "for i in df_Vestland_database['Aspect']:\n", " if (i== -1):\n", " aspect_categorical.append('Flat')\n", " elif (i<= 22.5) & (i >= 0) or (i>= 337.5) & (i < 360):\n", " aspect_categorical.append('North')\n", " elif (i<= 67.5) & (i > 22.5):\n", " aspect_categorical.append('North East')\n", " elif (i<= 112.5) & (i > 67.5):\n", " aspect_categorical.append('East')\n", " elif (i <= 157.5) & (i > 112.5):\n", " aspect_categorical.append('South East')\n", " elif (i <= 202.5) & (i > 157.5):\n", " aspect_categorical.append('South')\n", " elif (i<= 247.5) & (i > 202.5):\n", " aspect_categorical.append('South West')\n", " elif (i<= 292.5) & (i > 247.5):\n", " aspect_categorical.append('West')\n", " elif (i<= 337.5) & (i > 292.5):\n", " aspect_categorical.append('North West')\n", " else:\n", " aspect_categorical.append('NaN')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "df_Vestland_database['aspect_categorical'] = aspect_categorical" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "df_Vestland_database = df_Vestland_database.dropna()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[array(['East', 'Flat', 'North', 'North East', 'North West', 'South',\n", " 'South East', 'South West', 'West'], dtype=object)]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# encode aspect values\n", "\n", "from sklearn.preprocessing import OneHotEncoder\n", "aspect_cat = df_Vestland_database[[\"aspect_categorical\"]]\n", "\n", "aspect_encoder = OneHotEncoder()\n", "aspect_encoded = aspect_encoder.fit_transform(aspect_cat)\n", "\n", "aspect= aspect_encoded.toarray()\n", "aspect_encoder.categories_" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# sorted same as array \n", "aspect_groups = ('East','Flat','North','North East','North West','South','South East','South West','West')\n", "\n", "df_aspect = pd.DataFrame(aspect, columns=aspect_groups)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# adding Flat as a column as well\n", "#array = np.zeros([len(df_Vestland_database),1])\n", "#df_flat = pd.DataFrame(array, columns = ['Flat'])\n", "\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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indexOID_POINT_XPOINT_YElevationSlopeAspectCurvatureProfile_curvPlan_curv...BedrockMonzoniteDioriteAnorthositeGarnet mica schistMica gneissTonalitic gneissOrthopyroxene gneissMigmatiteaspect_categorical
00161314.57416.804053e+06785.76092542.66174394.2118533.051331-2.6368600.414471...1130.01.00.00.00.00.00.00.0East
11261324.57416.804053e+06775.54559344.81319494.6444020.9266360.7480651.674701...1130.01.00.00.00.00.00.00.0East
22361334.57416.804053e+06766.13720739.27142396.103744-1.3712162.4970101.125794...1130.01.00.00.00.00.00.00.0East
33461344.57416.804053e+06759.24694832.91645899.2305450.5354610.8431761.378637...1130.01.00.00.00.00.00.00.0East
44561354.57416.804053e+06753.40203929.343985103.7501220.7471920.3742381.121430...1130.01.00.00.00.00.00.00.0East
..................................................................
20479302562256256225769554.57416.816453e+06661.9188848.34364515.816636-2.9537351.943575-1.010160...1081.00.00.00.00.00.00.00.0North
20479312562257256225869564.57416.816453e+06661.9124769.20266021.7421880.1152340.3202420.435476...1081.00.00.00.00.00.00.00.0North
20479322562258256225969574.57416.816453e+06661.5366829.15245544.1412392.452026-0.8624581.589569...1081.00.00.00.00.00.00.00.0North East
20479332562259256226069584.57416.816453e+06659.75378410.50486662.2870790.2008670.0629480.263814...1081.00.00.00.00.00.00.00.0North East
20479342562260256226169594.57416.816453e+06657.85742211.79017568.847740-0.410706-0.070160-0.480866...1081.00.00.00.00.00.00.00.0East
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2047935 rows × 24 columns

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" ], "text/plain": [ " index OID_ POINT_X POINT_Y Elevation Slope \\\n", "0 0 1 61314.5741 6.804053e+06 785.760925 42.661743 \n", "1 1 2 61324.5741 6.804053e+06 775.545593 44.813194 \n", "2 2 3 61334.5741 6.804053e+06 766.137207 39.271423 \n", "3 3 4 61344.5741 6.804053e+06 759.246948 32.916458 \n", "4 4 5 61354.5741 6.804053e+06 753.402039 29.343985 \n", "... ... ... ... ... ... ... \n", "2047930 2562256 2562257 69554.5741 6.816453e+06 661.918884 8.343645 \n", "2047931 2562257 2562258 69564.5741 6.816453e+06 661.912476 9.202660 \n", "2047932 2562258 2562259 69574.5741 6.816453e+06 661.536682 9.152455 \n", "2047933 2562259 2562260 69584.5741 6.816453e+06 659.753784 10.504866 \n", "2047934 2562260 2562261 69594.5741 6.816453e+06 657.857422 11.790175 \n", "\n", " Aspect Curvature Profile_curv Plan_curv ... Bedrock \\\n", "0 94.211853 3.051331 -2.636860 0.414471 ... 113 \n", "1 94.644402 0.926636 0.748065 1.674701 ... 113 \n", "2 96.103744 -1.371216 2.497010 1.125794 ... 113 \n", "3 99.230545 0.535461 0.843176 1.378637 ... 113 \n", "4 103.750122 0.747192 0.374238 1.121430 ... 113 \n", "... ... ... ... ... ... ... \n", "2047930 15.816636 -2.953735 1.943575 -1.010160 ... 108 \n", "2047931 21.742188 0.115234 0.320242 0.435476 ... 108 \n", "2047932 44.141239 2.452026 -0.862458 1.589569 ... 108 \n", "2047933 62.287079 0.200867 0.062948 0.263814 ... 108 \n", "2047934 68.847740 -0.410706 -0.070160 -0.480866 ... 108 \n", "\n", " Monzonite Diorite Anorthosite Garnet mica schist Mica gneiss \\\n", "0 0.0 1.0 0.0 0.0 0.0 \n", "1 0.0 1.0 0.0 0.0 0.0 \n", "2 0.0 1.0 0.0 0.0 0.0 \n", "3 0.0 1.0 0.0 0.0 0.0 \n", "4 0.0 1.0 0.0 0.0 0.0 \n", "... ... ... ... ... ... \n", "2047930 1.0 0.0 0.0 0.0 0.0 \n", "2047931 1.0 0.0 0.0 0.0 0.0 \n", "2047932 1.0 0.0 0.0 0.0 0.0 \n", "2047933 1.0 0.0 0.0 0.0 0.0 \n", "2047934 1.0 0.0 0.0 0.0 0.0 \n", "\n", " Tonalitic gneiss Orthopyroxene gneiss Migmatite aspect_categorical \n", "0 0.0 0.0 0.0 East \n", "1 0.0 0.0 0.0 East \n", "2 0.0 0.0 0.0 East \n", "3 0.0 0.0 0.0 East \n", "4 0.0 0.0 0.0 East \n", "... ... ... ... ... \n", "2047930 0.0 0.0 0.0 North \n", "2047931 0.0 0.0 0.0 North \n", "2047932 0.0 0.0 0.0 North East \n", "2047933 0.0 0.0 0.0 North East \n", "2047934 0.0 0.0 0.0 East \n", "\n", "[2047935 rows x 24 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_Vestland_database" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# merging the one-hot-encoded bedrock dataframe to the other parameters\n", "#df_Vestland_database = df_Vestland_database.reset_index() #Need to do this, don't know why indexes was changed.\n", "df_Vestland_database = pd.concat([df_Vestland_database, df_aspect], axis=1)\n", "\n", "\n", "#df_Vestland_database = pd.concat([df_Vestland_database, df_flat], axis=1)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['index', 'OID_', 'POINT_X', 'POINT_Y', 'Elevation', 'Slope', 'Aspect',\n", " 'Profile_curv', 'Plan_curv', 'Flow_dir', 'Flow_acc',\n", " 'Distance_to_roads', 'TRI', 'Bedrock', 'Monzonite', 'Gabbro',\n", " 'Anorthosite', 'Phyllite', 'Tonalitic gneiss', 'Mica gneiss',\n", " 'Tonalitic gneiss', 'Orthopyroxene gneiss', 'Migmatite',\n", " 'aspect_categorical', 'East', 'Flat', 'North', 'North East',\n", " 'North West', 'South', 'South East', 'South West', 'West'],\n", " dtype='object')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_Vestland_database.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#df_Vestland_database['Flow_dir'] = 0" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# selcted features\n", "\n", "#Vestland_features = df_Vestland_database[['POINT_X', 'POINT_Y',\n", "# 'Elevation', 'Slope', 'Aspect', 'Profile_curv',\n", "# 'Plan_curv', 'Flow_dir', 'Flow_acc', 'Distance_to_roads', 'TRI',\n", "# 'Bedrock', 'Granite', 'Granodiorite', 'Tonalite', 'Trondhjemite',\n", "# 'Syenite', 'Monzonite', 'Monzodiorite', 'Quartz diorite', 'Diorite',\n", "# 'Gabbro', 'Norite', 'Peridotite', 'Pyroksenite', 'Charnockite',\n", "# 'Mangerite', 'Anorthosite', 'Mafic dyke (Diabase, Dolerite)',\n", "# 'Pegmatite/aplite', 'Felsic volcanic rock', 'Rhyolite', 'Dacite',\n", "# 'Intermediate volcanic rock', 'Andesite', 'Mafic volcanic rock',\n", "# 'Basalt', 'Pyroclastic rock', 'Volcanic breccia', 'Siltstone',\n", "# 'Sandstone', 'Greywacke', 'Arkose', 'Konglomerate',\n", "# 'Sedimentary breccia', 'Limestone', 'Tuffite', 'Shale', 'Phyllite',\n", "# 'Mica schist', 'Garnet mica schist', 'Calcareous phyllite',\n", "# 'Calcareous mica schist', 'Amphibole schist', 'Graphitic schist',\n", "# 'Calcite marble', 'Metasandstone', 'Metagreywacke', 'Meta-arkose',\n", "# 'Quartzite', 'Quartz schist', 'Mica gneiss', 'Calc-silicate rock',\n", "# 'Amphibole gneiss', 'Granitic gneiss', 'Granodioritic gneiss',\n", "# 'Tonalitic gneiss', 'Quartz dioritic gneiss', 'Monzonitic gneiss',\n", "# 'Dioritic gneis', 'Orthopyroxene gneiss', 'Migmatite', 'Augengneiss',\n", "# 'Banded gneiss', 'Greenschist', 'Greenstone', 'Amphibolite',\n", "# 'Metagabbro', 'Eclogite', 'Serpentinite', 'Mylonite/Phyllonite',\n", "# 'Cataclasite', 'aspect_categorical', 'East', 'North', 'North East',\n", "# 'North West', 'South', 'South East', 'South West', 'West', 'Flat']].copy()\n", " \n", " \n", "#Vestland_features = df_Vestland_database[['POINT_X','POINT_Y','Elevation', 'Slope', 'East', 'North', 'North East','North West', 'South', 'South East', 'South West', 'West', 'Flat', 'Profile_curv',\n", " # \"\"'Plan_curv', 'Flow_dir', 'Flow_acc', 'Distance_to_roads', 'TRI', 'Granite','Granodiorite','Monzonite','Monzodiorite','Quartx diorite','Gabbro','Pyroksenitt','Charnockitt','Anorthosite','Rhyolite','Phyllite','Calcareous phyllite','Metasandstone','Quartzite','Quartz schist','Mica gneiss','Amphibole gneiss','Granitic gneiss','Granodioritic gneiss','Tonalitic gneiss','Monzonitic gneiss','Orthopyroxene gneiss','Migmatite','Augengneiss','Banded gneiss','Amphibolite','Metagabbro','Mylonite/Phyllonite','Cataclasite']].copy()\n", "\n", "\n", "#target = df_Vestland_database[['POINT_X', 'POINT_Y']].copy()\n", "\n", "#Vestland_features = df_Vestland_database[['POINT_X', 'POINT_Y', 'Elevation',\n", " # 'Slope', 'Aspect', 'Profile_curv', 'Plan_curv', 'Flow_dir', 'Flow_acc',\n", " # 'Distance_to_roads', 'TRI', 'Bedrock', 'Granite', 'Monzonite',\n", " # 'Monzodiorite', 'Charnockite', 'Granitic gneiss', 'Tonalitic gneiss',\n", " #'aspect_categorical', 'East', 'North', 'North East', 'North West',\n", " #'#South', 'South East', 'South West', 'West', 'Flat']].copy()\n", " \n", " \n", "#Vestland_features = df_Vestland_database[['POINT_X', 'POINT_Y', 'Elevation',\n", " # 'Slope', 'Aspect', 'Profile_curv', 'Plan_curv', 'Flow_dir', 'Flow_acc',\n", " # 'Distance_to_roads', 'TRI', 'Bedrock', 'Monzonite','Monzodiorite','Charnockite','Anorthosite','Granitic gneiss','Tonalitic gneiss',\n", " # 'aspect_categorical', 'East', 'North', 'North East', 'North West',\n", " # 'South', 'South East', 'South West', 'West', 'Flat']].copy()\n", "\n", "Vestland_features = df_Vestland_database[['POINT_X', 'POINT_Y', 'Elevation',\n", " 'Slope', 'Aspect', 'Profile_curv', 'Plan_curv', 'Flow_dir', 'Flow_acc',\n", " 'Distance_to_roads', 'TRI', 'Bedrock', 'Monzonite','Gabbro','Anorthosite','Phyllite','Tonalitic gneiss','Mica gneiss','Tonalitic gneiss','Orthopyroxene gneiss','Migmatite',\n", " 'aspect_categorical', 'East', 'North', 'North East', 'North West',\n", " 'South', 'South East', 'South West', 'West', 'Flat']].copy()\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "#Vestland_features.to_csv(\"ML_Vestland_West_10_m_database.csv\")\n", "Vestland_features.to_csv('ML_bohme_20m.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "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": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 4 }