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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from pykrige import OrdinaryKriging"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"ok = OrdinaryKriging(data[:,0],data[:,1],data[:,2])\n",
"ok.ex"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"a,b = ok.execute('grid',x[0],y[:,0])"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"from pykrige import OrdinaryKriging\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"def ordinary_kriging(dataset, resolution='standard', coordinate_type='euclidean',verbose='False',method='grid', isvariance = False):\n",
" if coordinate_type == 'latlong_small':\n",
" \"\"\"\n",
" Assume that the Earth is a Sphere, and use polar coordinates\n",
" $| \\vec{r_2}− \\vec{r_1}| ≈ \\text{R }\\times \\sqrt[]{(Lat_2 - Lat_1)^{2} + (Long_2 - Long_1)^{2}}$\n",
" \"\"\"\n",
" return \"To be done later\"\n",
" if coordinate_type == 'latlong_large':\n",
" \"\"\"\n",
" Code to be written after understanding all the projections.\n",
" \"\"\"\n",
" return \"To be done later\"\n",
" if coordinate_type==\"euclidean\":\n",
" \n",
" ok = OrdinaryKriging(dataset[:,0],dataset[:,1],dataset[:,2])\n",
" X = dataset[:,0]\n",
" y = dataset[:,1]\n",
" \n",
" if resolution=='high':\n",
" xx,yy = make_grid(X,y,1000)\n",
" \n",
" elif resolution=='low':\n",
" xx,yy = make_grid(X,y,10)\n",
" \n",
" elif resolution=='standard':\n",
" xx,yy = make_grid(X,y,100)\n",
" \n",
" else:\n",
" print('Value Error - Resolution can only be one of \\nhigh, low or standard')\n",
" \n",
" values, variances = ok.execute(method, xx[0], yy[:,0])\n",
" \n",
" if isvariance:\n",
" return values, variances\n",
" else:\n",
" del variances\n",
" return np.array(values)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[129.94984945, 129.7682324 , 129.58820662, ..., 159.34079485,\n",
" 159.99175016, 160.63241067],\n",
" [130.22090025, 130.03615966, 129.8529146 , ..., 159.9575165 ,\n",
" 160.61228126, 161.25625641],\n",
" [130.50105231, 130.31324536, 130.12683652, ..., 160.59265384,\n",
" 161.25084023, 161.8977369 ],\n",
" ...,\n",
" [207.22133238, 207.82739139, 208.44615116, ..., 248.64646661,\n",
" 248.3790241 , 248.11033441],\n",
" [207.92838926, 208.53490708, 209.15376273, ..., 248.91678379,\n",
" 248.65601627, 248.39371596],\n",
" [208.61942088, 209.22595474, 209.84445913, ..., 249.17442481,\n",
" 248.9203453 , 248.66446245]])"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ordinary_kriging(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* What does ok('points') really do?\n",
"* Specifically test when points aren't really passed - they are let's say the point of an array\n",
"* Returns the diagonal matrix of all these coordinates"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([129.94984945, 130.03615966, 130.12683652, 130.22219703,\n",
" 130.32258826, 130.42839089, 130.54002324, 130.65794596,\n",
" 130.7826674 , 130.91474976, 131.05481629, 131.20355964,\n",
" 131.36175158, 131.53025441, 131.71003442, 131.90217771,\n",
" 132.107909 , 132.32861401, 132.56586607, 132.82145795,\n",
" 133.0974399 , 133.39616477, 133.72034153, 134.07309736,\n",
" 134.45804822, 134.87937482, 135.34189663, 135.85112772,\n",
" 136.41328222, 137.03517039, 137.72388496, 138.48612122,\n",
" 139.326921 , 140.24763047, 141.24300526, 142.29757046,\n",
" 143.37881815, 144.38425962, 144.49187978, 143.1202101 ,\n",
" 141.66667134, 140.45686022, 139.66795657, 142.48270308,\n",
" 147.03665055, 151.8487008 , 156.90272514, 162.25791164,\n",
" 168.04938768, 173.63870768, 180.93567147, 190.3440156 ,\n",
" 199.86834472, 208.48375248, 215.75635742, 222.1915652 ,\n",
" 228.08641413, 233.15249702, 236.89713686, 239.83524192,\n",
" 242.45744315, 244.57483343, 245.52139699, 245.88236757,\n",
" 246.12295211, 246.3306567 , 246.52369882, 246.70598807,\n",
" 246.87792737, 247.03919426, 247.18952217, 247.3288843 ,\n",
" 247.45749059, 247.57573348, 247.68412862, 247.78326467,\n",
" 247.87376505, 247.95626051, 248.03137024, 248.09968963,\n",
" 248.16178271, 248.21817801, 248.26936683, 248.31580309,\n",
" 248.35790422, 248.39605277, 248.43059841, 248.46186013,\n",
" 248.49012851, 248.51566797, 248.53871897, 248.55950011,\n",
" 248.57821004, 248.59502931, 248.61012204, 248.62363741,\n",
" 248.63571111, 248.64646661, 248.65601627, 248.66446245])"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ordinary_kriging(data,method='points')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def make_grid(X,y,res):\n",
" y_min = y.min()-0.2\n",
" y_max = y.max()+0.2\n",
" x_min = X.min()-0.2\n",
" x_max = X.max()+0.2\n",
" x_arr = np.linspace(x_min,x_max,res)\n",
" y_arr = np.linspace(y_min,y_max,res)\n",
" xx,yy = np.meshgrid(x_arr,y_arr) \n",
" return xx,yy\n",
"x, y = make_grid(data[:,0],data[:,1],100)"
]
}
],
"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": "3.6.8"
}
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"nbformat": 4,
"nbformat_minor": 2
}
|