test_loading_elevation_maps.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test loading elevation maps\n",
    "\n",
    "Goal is to capture all of the data in the maps, with NaNs when missing, and have a grid rotated alongshore to facilitate erosion calcs.\n",
    "\n",
    "This notebook served as a development platform to test the rotation and interpolation functions. As of Sept. 5, 2018, I was still confused by why the y axis increased northward but I had to flip the interpolated matrix.\n",
    "\n",
    "The buffering routine was tested in test_buffer_elevation_maps, but not here.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import fiona\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.signal\n",
    "from IPython.display import display, HTML\n",
    "import rasterio # rasterio and OGR don't mix...see rasterio docs\n",
    "from rasterio import Affine\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdir = \"F:/2017_Karen_Sandwich_maps\"\n",
    "#fdir = \"C:/crs/proj/2017_Karen_Sandwich_maps\"\n",
    "\n",
    "fnames = (\\\n",
    "\"2016-01-22_SandwichTNB_PT_DEM_10cm_trimmed.tif\",\\\n",
    "\"2016-01-25_SandwichTNB_DEM_10cm_trimmed.tif\",\\\n",
    "\"2016-02-11_SandwichTNB_DEM_10cm_trimmed.tif\",\\\n",
    "\"2016-03-30_SandwichTNB_AS_DEM_10cm_trimmed.tif\",\\\n",
    "\"2016-09-21_SandwichTNB_DEM_10cm_trimmed.tif\",\\\n",
    "\"2017-01-09_SandwichTNB_DEM_10cm_trimmed.tif\",\\\n",
    "\"2017-01-25_SandwichTNB_DEM_10cm_trimmed.tif\",\\\n",
    "\"2017-02-14_SandwichTNB_DEM_10cm_trimmed.tif\",\\\n",
    "\"2017-03-16_SandwichTNB_DEM_10cm_trimmed.tif\",\\\n",
    "\"2017-04-28_SandwichTNB_DEM_10cm_trimmed.tif\",\\\n",
    "\"2017-05-04_SandwichTNB_DEM_10cm_trimmed.tif\",\\\n",
    "\"2017-09-18_SandwichTNB_DEM_10cm_trimmed.tif\",\\\n",
    "#\"2018-01-03_SandwichTNB_DEM_10cm_trimmed.tif\",\\ # needs to be padded...not very good control\n",
    "\"2018-01-10_SandwichTNB_DEM_10cm_trimmed.tif\",\\\n",
    "\"DEM_10cm_selfcal_lokicampos_mid_denseNAD83UTM19.tif\")\n",
    "\n",
    "dslist =[]\n",
    "# just read one for now (note...this reads in as a DataArray, not a DataSet)\n",
    "#fn = fdir+\"/2016-01-22_SandwichTNB_PT_DEM_10cm_trimmed.tif\"\n",
    "fn = fdir+'/'+fnames[13]\n",
    "#print(fn)\n",
    "\n",
    "da = xr.open_rasterio(fn)\n",
    "#     ds = ds.sel(x=slice(e0,e1), y=slice(n0,n1))\n",
    "#     if len(dslist) == 0:\n",
    "#         xc = ds['x']-ds['x'].values.min()\n",
    "#         yc = ds['y']-ds['y'].values.min()\n",
    "#     ds['x'] = xc\n",
    "#     ds['y'] = yc\n",
    "#     dslist.append(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "375796.008388 378032.808388 4625746.32523 4623915.32523\n",
      "-32767.0 16.1258\n",
      "Frozen(OrderedDict([('band', 1), ('y', 18311), ('x', 22369)]))\n",
      "('band', 'y', 'x')\n",
      "band: Int64Index([1], dtype='int64', name='band')\n",
      "y: Float64Index([4625746.3252296215,  4625746.225229621,  4625746.125229621,\n",
      "                  4625746.025229622,  4625745.925229621, 4625745.8252296215,\n",
      "                  4625745.725229621,  4625745.625229621,  4625745.525229622,\n",
      "                  4625745.425229621,\n",
      "                 ...\n",
      "                  4623916.225229622,  4623916.125229621,  4623916.025229622,\n",
      "                  4623915.925229622, 4623915.8252296215,  4623915.725229622,\n",
      "                  4623915.625229621,  4623915.525229622,  4623915.425229622,\n",
      "                 4623915.3252296215],\n",
      "                dtype='float64', name='y', length=18311)\n",
      "x: Float64Index([375796.00838793354,  375796.1083879336, 375796.20838793356,\n",
      "                 375796.30838793353, 375796.40838793357, 375796.50838793354,\n",
      "                  375796.6083879336, 375796.70838793356, 375796.80838793353,\n",
      "                 375796.90838793357,\n",
      "                 ...\n",
      "                 378031.90838793357,  378032.0083879336,  378032.1083879336,\n",
      "                 378032.20838793356,  378032.3083879336, 378032.40838793357,\n",
      "                  378032.5083879336,  378032.6083879336, 378032.70838793356,\n",
      "                  378032.8083879336],\n",
      "                dtype='float64', name='x', length=22369)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[-32767., -32767., -32767., ..., -32767., -32767., -32767.],\n",
       "        [-32767., -32767., -32767., ..., -32767., -32767., -32767.],\n",
       "        [-32767., -32767., -32767., ..., -32767., -32767., -32767.],\n",
       "        ..., \n",
       "        [-32767., -32767., -32767., ..., -32767., -32767., -32767.],\n",
       "        [-32767., -32767., -32767., ..., -32767., -32767., -32767.],\n",
       "        [-32767., -32767., -32767., ..., -32767., -32767., -32767.]]], dtype=float32)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look to see what got loaded\n",
    "print(da['x'].values[0], da['x'].values[-1], da['y'].values[0],\\\n",
    "      da['y'].values[-1])\n",
    "# this is super slow and only successfully returns min and max\n",
    "# print(stats.describe(da.values,nan_policy='omit'))\n",
    "print(da.values.min(), da.values.max())\n",
    "print(da.sizes)\n",
    "print(da.dims)\n",
    "print(da.indexes)\n",
    "da.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example from scipy.interpolate.RegularGridInterpolator documentation\n",
    "```\n",
    "def ff(x,y):\n",
    "...     return 2 * x**3 + 3 * y**2\n",
    "x = np.linspace(1, 4, 11)\n",
    "y = np.linspace(4, 7, 22)\n",
    "data = ff(*np.meshgrid(x, y, indexing='ij', sparse=True))\n",
    "print(np.shape(x),np.shape(y),np.shape(data))\n",
    "\n",
    "f = interpolate.RegularGridInterpolator( (x, y), data, method='nearest')\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of y, x, and da.values:  (18311,) (22369,) (1, 18311, 22369)\n",
      "Shape of x, y, and zs (22369,) (18311,) (18311, 22369)\n"
     ]
    }
   ],
   "source": [
    "from scipy import interpolate\n",
    "print(\"shape of y, x, and da.values: \",\\\n",
    "      np.shape(np.flipud(da['y'].values)), np.shape(da['x'].values),\\\n",
    "      np.shape( np.flipud(da.values)) )\n",
    "x = da['x'].values\n",
    "y = np.flipud(da['y'].values)\n",
    "\n",
    "# Not sure how da.values got a singleton dimension, but squeeze gets rid of it.\n",
    "# However, make sure to squeeze before flipping\n",
    "z = np.flipud(np.squeeze(da.values))\n",
    "\n",
    "# smooth with 2D running median\n",
    "zs = scipy.signal.medfilt2d(z, kernel_size=3)\n",
    "\n",
    "print(\"Shape of x, y, and zs\",np.shape(x),np.shape(y),np.shape(zs))\n",
    "f = interpolate.RegularGridInterpolator( (y, x), zs, method='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xrot:\n",
      "(1300, 280)\n",
      "[[   0.5    1.5    2.5 ...,  277.5  278.5  279.5]\n",
      " [   0.5    1.5    2.5 ...,  277.5  278.5  279.5]\n",
      " [   0.5    1.5    2.5 ...,  277.5  278.5  279.5]\n",
      " ..., \n",
      " [   0.5    1.5    2.5 ...,  277.5  278.5  279.5]\n",
      " [   0.5    1.5    2.5 ...,  277.5  278.5  279.5]\n",
      " [   0.5    1.5    2.5 ...,  277.5  278.5  279.5]]\n",
      "yrot:\n",
      "(1300, 280)\n",
      "[[  5.00000000e-01   5.00000000e-01   5.00000000e-01 ...,   5.00000000e-01\n",
      "    5.00000000e-01   5.00000000e-01]\n",
      " [  1.50000000e+00   1.50000000e+00   1.50000000e+00 ...,   1.50000000e+00\n",
      "    1.50000000e+00   1.50000000e+00]\n",
      " [  2.50000000e+00   2.50000000e+00   2.50000000e+00 ...,   2.50000000e+00\n",
      "    2.50000000e+00   2.50000000e+00]\n",
      " ..., \n",
      " [  1.29750000e+03   1.29750000e+03   1.29750000e+03 ...,   1.29750000e+03\n",
      "    1.29750000e+03   1.29750000e+03]\n",
      " [  1.29850000e+03   1.29850000e+03   1.29850000e+03 ...,   1.29850000e+03\n",
      "    1.29850000e+03   1.29850000e+03]\n",
      " [  1.29950000e+03   1.29950000e+03   1.29950000e+03 ...,   1.29950000e+03\n",
      "    1.29950000e+03   1.29950000e+03]]\n"
     ]
    }
   ],
   "source": [
    "# make a small box w/ cell centered grid with spacing dxy\n",
    "dxy = .2\n",
    "xmax = 280. # alongshore\n",
    "ymax = 1300.  # cross-shore\n",
    "nx = int((1./dxy)*xmax)\n",
    "ny = int((1./dxy)*ymax)\n",
    "\n",
    "# Y is decending\n",
    "#xrot, yrot=np.meshgrid(np.linspace(0.,xmax,nx), np.flip(np.linspace(0.,ymax,ny),0) ,sparse=False, indexing='xy')\n",
    "# Y is ascending\n",
    "xrot, yrot=np.meshgrid(np.linspace(0.5*dxy,xmax-0.5*dxy,nx), np.linspace(0.5*dxy,ymax-0.5*dxy,ny) ,sparse=False, indexing='xy')\n",
    "print(\"xrot:\")\n",
    "print(xrot.shape)\n",
    "print(xrot)\n",
    "print(\"yrot:\")\n",
    "print(yrot.shape)\n",
    "print(yrot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.25355339 -0.0767767  -0.96066017 -0.78388348  0.1       ] [ 1.16066017  1.33743687  1.86776695  2.04454365  1.51421356]\n",
      "[ 0.1] [ 1.51421356]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-3, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAADCxJREFUeJzt3V2IXHcdxvHn2SS1Tqr0woVqk51R\nLLYhFNsMRUFqxaixSIuCUBEs9GIRfGnBC18CbqIsKIUiaEECLQi7KkINldrSF7BUL/qykTYmJC1R\nkjZW2lWpWgqWmJ8XM5WN2Z2Z7fnvObP7+35gYM7Z/57zkN08OfP/n8k4IgQgr4mmAwBoFiUAJEcJ\nAMlRAkBylACQHCUAJFe5BGxfaPtJ28/YPmp7f4lgAOrhqvcJ2LakrRHxqu0tkn4n6daIeLxEQABr\na3PVA0SvRV7tb27pP7gDCVgnKpeAJNneJOmQpPdKujMinlhmzLSkaUnaunXrrssvv7zEqQEs49Ch\nQ3+NiMlRxlZ+OXDOweyLJR2U9JWIOLLSuG63GwsLC8XOC+Bctg9FRHeUsUVXByLiFUmPStpT8rgA\n1k6J1YHJ/hWAbL9V0m5Jx6seF0A9SswJvFPST/rzAhOSfhER9xU4LoAalFgdOCzpqgJZADSAOwaB\n5CgBIDlKAEiOEgCSowSA5CgBIDlKAEiOEgCSowSA5CgBIDlKAEiOEgCSowSA5CgBIDlKAEiOEgCS\nowSA5CgBIDlKAEiOEgCSowSA5CgBIDlKAEiOEgCSowSA5CgBIDlKYB3at6/pBNhISnwq8Xbbv7F9\nzPZR27eWCIaV7d/fdAJsJCWuBM5I+lpEXCHpA5K+ZHtHgeNiA5mfn1en09HExIQ6nY7m5+ff1Bis\ngYgo+pB0r6SPDRqza9euwOrMzERI5z9mZppONtzc3Fy0Wq2Q9L9Hq9WKubm5VY3B6CQtxIh/Z90b\nX4btjqTHJO2MiH+uNK7b7cbCwkKx82Zj9ypgveh0Ojp16tR5+9vttk6ePDnyGIzO9qGI6I4yttjE\noO2LJN0j6bblCsD2tO0F2wuLi4ulTot14Pnnnx+6f5QxWBtFSsD2FvUKYD4ifrncmIg4EBHdiOhO\nTk6WOO2Gt9IqwMxMrTEqm5qaGrp/lDFYGyVWByzpLknHIuKO6pHwhpVWAdbbEuHs7KxardY5+1qt\nlmZnZ1c1Bmtk1MmDlR6SPqTeRM5hSU/3H9cP+h4mBkcjNZ2gnLm5uWi322E72u32shN+o4zBaNTU\nxOComBhc2b59y18BzMysvysANGc1E4OUwBhbb6sAGB+NrA4AWJ8ogTG23lYBsD5RAmOMOQDUgRIA\nkqMEgOQoASA5SgBIjhIAkqMEsGqsWmwslABWjf/ebGOhBIDkKAGMZN++3nsZ7N72G895abD+8QYi\nrBpvbBp/vIEIwMgoAawab2zaWCgBrGil1/vMA2wslABWxFJgDpQAkBwlgHOwFJgPS4RYEUuB6xdL\nhABGRgkkN+gyn6XAHHg5kByX/BsTLwcAjIwSSIgVACzFy4HkeDmwMfFyAMDIipSA7bttv2z7SInj\noT51rQDMz8+r0+loYmJCnU5H8/PzazYGqzTqxxcPeki6VtLVko6MMp6PJs9lbm4uWq1WqPcR9iEp\nWq3WOR89XmoMetTER5Pb7ki6LyJ2DhvLnEAunU5Hp06dOm9/u93WyZMni45BTyMfTT6sBGxPS5qW\npKmpqV3L/TCxMU1MTGi53zPbOnv2bNEx6BnLicGIOBAR3YjoTk5O1nVajIGpqamh+0uNweqxOoA1\nNzs7q1ardc6+Vqul2dnZ4mPwJow6eTDsIakjJgaxgrm5uWi322E72u32spN5pcaggYlB2z+TdJ2k\nd0h6SdJMRNy10ngmBoG1tZo5gc0lThgRnytxHAD1Y04ASI4SAJKjBIDkKAEgOUoASI4SAJKjBIDk\nKAEgOUoASI4SAJKjBIDkKAEgOUoASI4SAJKjBIDkKAEgOUoASI4SAJKjBIDkKAEgOUoASI4SAJKj\nBIDkKAEgOUoASI4SAJKjBIDkipSA7T22n7V9wvY3ShwTQD0ql4DtTZLulPRJSTskfc72jqrHBVCP\nElcC10g6ERF/iojXJf1c0o0FjgugBiVK4FJJLyzZPt3fdw7b07YXbC8sLi4WOC2AEkqUgJfZF+ft\niDgQEd2I6E5OThY4LYASSpTAaUnbl2xvk/RigeMCqEGJEnhK0mW23237Akk3SfpVgeMCqMHmqgeI\niDO2vyzpQUmbJN0dEUcrJwNQi8olIEkRcb+k+0scC0C9uGMQSI4SAJKjBIDkKAEgOUoASI4SAJKj\nBIDkKAEgOUoASI4SAJKjBIDkKAEgOUoASI4SAJKjBIDkKAEgOUoASI4SAJKjBIDkKAEgOUoASI4S\nAJKjBIDkKAEgOUoASI4SAJKjBIDkKpWA7c/aPmr7rO1uqVAA6lP1SuCIpM9IeqxAFgANqPSpxBFx\nTJJsl0kDoHa1zQnYnra9YHthcXGxrtMCGGLolYDtRyRdssyX9kbEvaOeKCIOSDogSd1uN0ZOCGBN\nDS2BiNhdRxAAzWCJEEiu6hLhp22flvRBSb+2/WCZWADqUnV14KCkg4WyAGgALweA5CgBIDlKAEiO\nEgCSowSA5CgBIDlKAEiOEgCSowSA5CgBIDlKAEiOEgCSowSA5CgBIDlKAEiOEgCSowSA5CgBIDlK\nAEiOEgCSowSA5CgBIDlKAEiOEgCSowSA5CgBIDlKAEiu6geS3m77uO3Dtg/avrhUMAD1qHol8LCk\nnRFxpaTnJH2zeiQAdapUAhHxUESc6W8+Lmlb9UgA6lRyTuAWSQ8UPB6AGmweNsD2I5IuWeZLeyPi\n3v6YvZLOSJofcJxpSdOSNDU19abCAihvaAlExO5BX7d9s6RPSfpoRMSA4xyQdECSut3uiuMA1Gto\nCQxie4+kr0v6cES8ViYSgDpVnRP4kaS3SXrY9tO2f1wgE4AaVboSiIj3lgoCoBncMQgkRwkAyVEC\nQHKUAJAcJQAkRwkAyVECQHKUAJAcJQAkRwkAyVECQHKUAJAcJQAkRwkAyVECQHKUAJAcJQAkRwkA\nyVECQHKUAJAcJQAkRwkAyVECQHKUAJAcJQAkRwkAyVECQHKUAJBcpRKw/V3bh/ufSPyQ7XeVCgag\nHlWvBG6PiCsj4v2S7pP07QKZANSoUglExD+XbG6VFNXiAKjb5qoHsD0r6QuS/iHpIwPGTUua7m/+\n2/aRqucu6B2S/tp0iCXGLY80fpnIM9j7Rh3oiMH/eNt+RNIly3xpb0Tcu2TcNyVdGBEzQ09qL0RE\nd9SQa408w41bJvIMtpo8Q68EImL3iOf9qaRfSxpaAgDGR9XVgcuWbN4g6Xi1OADqVnVO4Hu23yfp\nrKRTkr444vcdqHje0sgz3LhlIs9gI+cZOicAYGPjjkEgOUoASK6xEhi3W45t3277eD/TQdsXN5zn\ns7aP2j5ru7GlJ9t7bD9r+4TtbzSVY0meu22/PC73mdjebvs3to/1f163NpznQttP2n6mn2f/0G+K\niEYekt6+5PlXJf24qSz9DB+XtLn//PuSvt9wnivUu+HjUUndhjJskvRHSe+RdIGkZyTtaPjP5VpJ\nV0s60mSOJXneKenq/vO3SXquyT8jSZZ0Uf/5FklPSPrAoO9p7EogxuyW44h4KCLO9Dcfl7St4TzH\nIuLZJjNIukbSiYj4U0S8Lunnkm5sMlBEPCbp701mWCoi/hIRv+8//5ekY5IubTBPRMSr/c0t/cfA\nv1uNzgnYnrX9gqTPa7zefHSLpAeaDjEGLpX0wpLt02rwF3zc2e5Iukq9f32bzLHJ9tOSXpb0cEQM\nzLOmJWD7EdtHlnncKEkRsTcitkual/TltcwySp7+mL2SzvQzNZ6nYV5mH2vKy7B9kaR7JN32f1e5\ntYuI/0Tvnb3bJF1je+eg8ZXfQDQkzFjdcjwsj+2bJX1K0kej/6KqyTxj4LSk7Uu2t0l6saEsY8v2\nFvUKYD4iftl0njdExCu2H5W0R9KKE6lNrg6M1S3HtvdI+rqkGyLitSazjJGnJF1m+922L5B0k6Rf\nNZxprNi2pLskHYuIO8Ygz+QbK1u23yppt4b83WrsjkHb96g3+/2/W44j4s+NhOnlOSHpLZL+1t/1\neESMehv0WuT5tKQfSpqU9IqkpyPiEw3kuF7SD9RbKbg7ImbrzvB/eX4m6Tr13rr7kqSZiLirwTwf\nkvRbSX9Q73dZkr4VEfc3lOdKST9R7+c1IekXEfGdgd/TVAkAGA/cMQgkRwkAyVECQHKUAJAcJQAk\nRwkAyVECQHL/Baihi67LM/iNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1925b6cf710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test rotation matrix\n",
    "\n",
    "#some points\n",
    "xx = np.array((.5,.75,.5,.75, 1.)).T\n",
    "yy = np.array((1., 1., 2., 2., 1.)).T\n",
    "e0 = .1\n",
    "n0 = .1\n",
    "\n",
    "theta = 45. # degrees\n",
    "\n",
    "def box2UTMh(x, y, x0, y0, theta):\n",
    "    '''\n",
    "    2D rotation and translation of x, y\n",
    "    Input:\n",
    "        x, y - row vectors of original coordinates (must be same size)\n",
    "        x0, y0 - Offset (location of x, y = (0,0) in new coordinate system)\n",
    "        theta - Angle of rotation (degrees, CCW from x-axis == Cartesian coorinates)\n",
    "    Returns:\n",
    "        xr, yr - rotated, offset coordinates\n",
    "    '''\n",
    "    thetar = np.radians(theta)\n",
    "    c, s = np.cos(thetar), np.sin(thetar)\n",
    "\n",
    "    # homogenous rotation matrix\n",
    "    Rh = np.array(((c, -s,  0.),\\\n",
    "                   (s,  c,  0.),\\\n",
    "                   (0., 0., 1.)))\n",
    "    # homogenous translation matrix\n",
    "    Th = np.array(((1., 0., x0),\\\n",
    "                   (0., 1., y0),\\\n",
    "                   (0., 0., 1.)))\n",
    "    \n",
    "    # homogenous input x,y\n",
    "    xyh = np.vstack((x,y,np.ones_like(x)))\n",
    "\n",
    "    # perform rotation and translation\n",
    "    xyrh=np.matmul(np.matmul(Th,Rh),xyh)\n",
    "    xr = xyrh[0,:]\n",
    "    yr = xyrh[1,:]\n",
    "    return xr, yr\n",
    "\n",
    "# Test with horiz arrays\n",
    "xr, yr = box2UTMh( xx, yy, e0, n0, theta)\n",
    "print(xr,yr)\n",
    "\n",
    "# Test with single point\n",
    "xrr, yrr = box2UTMh( 1., 1., e0, n0, theta)\n",
    "print(xrr, yrr)\n",
    "\n",
    "plt.plot(figsize=(4,4))\n",
    "plt.plot(xx,yy,'ok')\n",
    "plt.plot(xr,yr,'+b')\n",
    "plt.axis('square')\n",
    "plt.xlim([-3,3])\n",
    "plt.ylim([-3,3])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corners of rotated coord. matrix in UTM:\n",
      "Lower left:  0.5 0.5 [ 377269.91382536] [ 4624440.70183611]\n",
      "Upper left:  0.5 1299.5 [ 376246.28785643] [ 4625240.44609256]\n",
      "Upper right: 279.5 0.5 [ 376418.05740804] [ 4625460.30109282]\n",
      "Lower right: 279.5 1299.5 [ 377441.68337698] [ 4624660.55683637]\n",
      "Shape of xrot:  (1300, 280)\n",
      "Shape of xu and yu:  (1300, 280) (1300, 280)\n"
     ]
    }
   ],
   "source": [
    "# calculate UTM coordinates for the box, which depend  the lower left-hand corner and rotation\n",
    "\n",
    "# bottom left corner of region of interest (UTM Zone 19N meters)\n",
    "e0 = 377270.; n0 = 4624440.\n",
    "rot = 52.\n",
    "def box2UTM(x, y, e0, n0, rot):\n",
    "    # for counterclockwise rotation (right-handed cartesian coords)\n",
    "    r = np.pi*(90-rot)/180.\n",
    "    xnew = x*np.cos(r) - y*np.sin(r) + e0\n",
    "    ynew = y*np.sin(r) + x*np.cos(r) + n0\n",
    "    return xnew, ynew\n",
    "\n",
    "# Check using values from corners\n",
    "xr_ll, yr_ll = xrot[0,      0], yrot[0,       0]\n",
    "xr_ul, yr_ul = xrot[ny-1,   0], yrot[ny-1,    0]\n",
    "xr_lr, yr_lr = xrot[0,   nx-1], yrot[0,    nx-1]\n",
    "xr_ur, yr_ur = xrot[ny-1,nx-1], yrot[ny-1, nx-1]\n",
    "xu_ll, yu_ll = box2UTMh( xr_ll, yr_ll, e0, n0, rot)\n",
    "xu_ul, yu_ul = box2UTMh( xr_ul, yr_ul, e0, n0, rot)\n",
    "xu_lr, yu_lr = box2UTMh( xr_lr, yr_lr, e0, n0, rot)\n",
    "xu_ur, yu_ur = box2UTMh( xr_ur, yr_ur, e0, n0, rot)\n",
    "\n",
    "print('Corners of rotated coord. matrix in UTM:')\n",
    "print('Lower left: ', xr_ll, yr_ll, xu_ll, yu_ll)\n",
    "print('Upper left: ', xr_ul, yr_ul, xu_ul, yu_ul)\n",
    "print('Upper right:', xr_lr, yr_lr, xu_ur, yu_ur)\n",
    "print('Lower right:', xr_ur, yr_ur, xu_lr, yu_lr)\n",
    "xub = np.array((xu_ll, xu_ul, xu_ur, xu_lr))\n",
    "yub = np.array((yu_ll, yu_ul, yu_ur, yu_lr))\n",
    "\n",
    "print('Shape of xrot: ',np.shape(xrot))\n",
    "shp = np.shape(xrot)\n",
    "xu, yu = box2UTMh(xrot.flatten(), yrot.flatten(), e0, n0, rot)\n",
    "xu=np.reshape(xu,shp)\n",
    "yu=np.reshape(yu,shp)\n",
    "print('Shape of xu and yu: ',np.shape(xu),np.shape(yu))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write out the box corners...maybe to look at them in GIS\n",
    "fid = open('F:/2017_Karen_Sandwich_maps/box_corners.txt','w')\n",
    "for i in range(0,4):\n",
    "    fid.write('{0:0.3f}, {1:0.3f}\\n'.format( *xub[i,:], *yub[i,:] ))\n",
    "fid.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1925c482f28>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S730mIuItIptEZI71WERkkojsFJFYEXnCar9LRLZatzUi0txpGftEZJuIbBaRSKf2N0Vk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1925be2a080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xub, yub,'-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18311,) (22369,) (1, 18311, 22369)\n",
      "(22369,) (18311,) (18311, 22369)\n"
     ]
    }
   ],
   "source": [
    "from scipy import interpolate\n",
    "print( np.shape(np.flipud(da['y'].values)), np.shape(da['x'].values), np.shape( np.flipud(da.values)) )\n",
    "x = da['x'].values\n",
    "y = np.flipud(da['y'].values)\n",
    "# Not sure how da.values got a singleton dimension, but squeeze gets rid of it.\n",
    "# However, make sure to squeeze before flipping\n",
    "z = np.flipud(np.squeeze(da.values))\n",
    "print(np.shape(x),np.shape(y),np.shape(z))\n",
    "f = interpolate.RegularGridInterpolator( (y, x), z, method='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1300, 280)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X0VhWOu/ufg3v9xtOoMLULu0ZdOMxvLL9Rpw19jX2OvkLWixII6L4vkPgCwur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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1925789f438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z[z<=-100.]=np.NaN\n",
    "plt.pcolormesh(x,y,z)\n",
    "plt.plot(xub,yub,'-r')\n",
    "plt.colorbar()\n",
    "np.shape(xu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "%%time\n",
    "zi = f((yu,xu))\n",
    "zi[zi<=-100.]=np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 51.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Array for interpolated elevations\n",
    "zi=np.NaN*np.ones((ny,nx))\n",
    "\n",
    "# this is a slow iteration through all of the points, but allows us to skip ones that are outside\n",
    "for i in np.ndindex(zi.shape):\n",
    "    try:\n",
    "        zi[i]=f((yu[i],xu[i]))\n",
    "    except:\n",
    "        zi[i]=np.NaN\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1925c5ab5c0>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5ab748>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5ab898>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5ab9e8>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5abb38>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5abc88>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5abdd8>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5abf28>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b40b8>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b4208>,\n",
       " <matplotlib.lines.Line2D at 0x1925c581cc0>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b4470>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b45c0>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b4710>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b4860>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b49b0>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b4b00>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b4c50>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b4da0>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b4ef0>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b9080>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b91d0>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b9320>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b9470>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b95c0>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b9710>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b9860>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b99b0>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b9b00>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b9c50>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b9da0>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5b9ef0>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5be080>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5be1d0>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5be320>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5be470>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5be5c0>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5be710>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5be860>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5be9b0>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5beb00>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5bec50>,\n",
       " <matplotlib.lines.Line2D at 0x1925c5beda0>,\n",
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5ZWKCNx76E3vH+zF9Tws2E6r5+sxZRu2JxMUviLUzey833I2ku6tluMcVRGET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\ni4JmMqdyAQmVHWGbV/ckg1dPsQvbgauG4TUolw9HNpNcU2q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reef4uF4x3OeufwIfr5t7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1925c4d3908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xu,yu,'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1925c810fd0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PGDMPPAd4Vdas7eMydh6bndDjxxizCDwV+AJwpgBt/n4g3+wgcJ/a7f58md/W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\nvYRSYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1925b6c6f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(figsize=(2,8))\n",
    "plt.pcolormesh(yrot,xrot,np.flipud(zi),cmap=plt.cm.terrain,vmin=-2,vmax=8)\n",
    "#plt.gca().invert_yaxis()\n",
    "#plt.axis('equal')\n",
    "plt.ylim(0,250)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1300, 280)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-2, 8)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3UdWzLMvMXjazlT3fMyNdZ2+Y2f1mVmNmiz+2bJ99sW6392zDhWY2IXKVH9h+\n+nWzmVX3bLcFZnbmx567sadfy83sc5GpunfMrMjMXjezpWa2xMyu61ke1dvtAP3yxHbrNedcVHwB\nccBqoBQIAB8CoyNdVwj9WQfkfGLZb4Eben6+AfhNpOvsZV9OBCYAiw/WF+BM4EW674U9BZgT6foP\nsV83A9/fx7qje96TCcDwnvdqXKT7cIC+FQATen4eRPclQUZH+3Y7QL88sd16+xVNI/ZJwCrn3Brn\nXDvwOHBehGsKt/OAh3p+fgg4P4K19Jpz7i2g7hOL99eX84CHXbfZQIaZFfRPpYdmP/3an/OAx51z\nbc65tcAqut+zA5Jzbotzbn7Pz43AUmAoUb7dDtCv/Ymq7dZb0RTsQ4GNH3u8iQNvsIHOATPNbF7P\n5RYA8pxzW6D7DQoMjlh1odtfX7ywHb/VMx1x/8emy6K2X2Y2DBgPzMFD2+0T/QKPbbcDiaZgt30s\ni+ZjNY93zk0AzgCuNbMTI11QP4n27XgXUAYcDWwB/tCzPCr7ZWapwNPA9c65XQdadR/LBmz/9tEv\nT223g4mmYN8EFH3scSGwOUK1hMw5t7nnew3wLN0f/7b9++Ntz/eayFUYsv31Jaq3o3Num3OuyzkX\nBP6X/3xsj7p+mVk83eH3mHPumZ7FUb/d9tUvL2233oimYJ8LlJvZcDMLAJcCMyJc02ExsxQzG/Tv\nn4HTgMV09+crPat9BXguMhWGxf76MgP4cs9RFlOAhn9/9I8Gn5hXvoDu7Qbd/brUzBLMbDhQDrzf\n3/X1lpkZcB+w1Dl368eeiurttr9+eWW79Vqk994eyhfde+ZX0L3n+qZI1xNCP0rp3hP/IbDk330B\nsoFXgZU937MiXWsv+/M3uj/edtA9Arpyf32h+6PvnT3bcBFQGen6D7Ffj/TUvZDuUCj42Po39fRr\nOXBGpOs/SN9OoHvKYSGwoOfrzGjfbgfolye2W2+/dEkBERGPiaapGBER6QUFu4iIxyjYRUQ8RsEu\nIuIxCnYREY9RsIuIeIyCXUTEY/4/2Y1I0B/4jx8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1925be22748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(np.shape(zi))\n",
    "plt.plot(zi[0,:])\n",
    "plt.ylim(-2,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1925d3fe860>,\n",
       " <matplotlib.lines.Line2D at 0x1925d3fe9e8>,\n",
       " <matplotlib.lines.Line2D at 0x1925d3feb38>,\n",
       " <matplotlib.lines.Line2D at 0x1925d3fec88>,\n",
       " <matplotlib.lines.Line2D at 0x1925d3fedd8>,\n",
       " <matplotlib.lines.Line2D at 0x1925d3fef28>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4060b8>,\n",
       " <matplotlib.lines.Line2D at 0x1925d406208>,\n",
       " <matplotlib.lines.Line2D at 0x1925d406358>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4064a8>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4fe8d0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d406710>,\n",
       " <matplotlib.lines.Line2D at 0x1925d406860>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4069b0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d406b00>,\n",
       " <matplotlib.lines.Line2D at 0x1925d406c50>,\n",
       " <matplotlib.lines.Line2D at 0x1925d406da0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d406ef0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d40b080>,\n",
       " <matplotlib.lines.Line2D at 0x1925d40b1d0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d40b320>,\n",
       " <matplotlib.lines.Line2D at 0x1925d40b470>,\n",
       " <matplotlib.lines.Line2D at 0x1925d40b5c0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d40b710>,\n",
       " <matplotlib.lines.Line2D at 0x1925d40b860>,\n",
       " <matplotlib.lines.Line2D at 0x1925d40b9b0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d40bb00>,\n",
       " <matplotlib.lines.Line2D at 0x1925d40bc50>,\n",
       " <matplotlib.lines.Line2D at 0x1925d40bda0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d40bef0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d411080>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4111d0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d411320>,\n",
       " <matplotlib.lines.Line2D at 0x1925d411470>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4115c0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d411710>,\n",
       " <matplotlib.lines.Line2D at 0x1925d411860>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4119b0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d411b00>,\n",
       " <matplotlib.lines.Line2D at 0x1925d411c50>,\n",
       " <matplotlib.lines.Line2D at 0x1925d411da0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d411ef0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d414080>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4141d0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d414320>,\n",
       " <matplotlib.lines.Line2D at 0x1925d414470>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4145c0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d414710>,\n",
       " <matplotlib.lines.Line2D at 0x1925d414860>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4149b0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d414b00>,\n",
       " <matplotlib.lines.Line2D at 0x1925d414c50>,\n",
       " <matplotlib.lines.Line2D at 0x1925d414da0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d414ef0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d41b080>,\n",
       " <matplotlib.lines.Line2D at 0x1925d41b1d0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d41b320>,\n",
       " <matplotlib.lines.Line2D at 0x1925d41b470>,\n",
       " <matplotlib.lines.Line2D at 0x1925d41b5c0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d41b710>,\n",
       " <matplotlib.lines.Line2D at 0x1925d41b860>,\n",
       " <matplotlib.lines.Line2D at 0x1925d41b9b0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d41bb00>,\n",
       " <matplotlib.lines.Line2D at 0x1925d41bc50>,\n",
       " <matplotlib.lines.Line2D at 0x1925d41bda0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d41bef0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d420080>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4201d0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d420320>,\n",
       " <matplotlib.lines.Line2D at 0x1925d420470>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4205c0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d420710>,\n",
       " <matplotlib.lines.Line2D at 0x1925d420860>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4209b0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d420b00>,\n",
       " <matplotlib.lines.Line2D at 0x1925d420c50>,\n",
       " <matplotlib.lines.Line2D at 0x1925d420da0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d420ef0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d424080>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4241d0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d424320>,\n",
       " <matplotlib.lines.Line2D at 0x1925d424470>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4245c0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d424710>,\n",
       " <matplotlib.lines.Line2D at 0x1925d424860>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4249b0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d424b00>,\n",
       " <matplotlib.lines.Line2D at 0x1925d424c50>,\n",
       " <matplotlib.lines.Line2D at 0x1925d424da0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d424ef0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d42a080>,\n",
       " <matplotlib.lines.Line2D at 0x1925d42a1d0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d42a320>,\n",
       " <matplotlib.lines.Line2D at 0x1925d42a470>,\n",
       " <matplotlib.lines.Line2D at 0x1925d42a5c0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d42a710>,\n",
       " <matplotlib.lines.Line2D at 0x1925d42a860>,\n",
       " <matplotlib.lines.Line2D at 0x1925d42a9b0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d42ab00>,\n",
       " <matplotlib.lines.Line2D at 0x1925d42ac50>,\n",
       " <matplotlib.lines.Line2D at 0x1925d42ada0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d42aef0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d430080>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4301d0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d430320>,\n",
       " <matplotlib.lines.Line2D at 0x1925d430470>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4305c0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d430710>,\n",
       " <matplotlib.lines.Line2D at 0x1925d430860>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4309b0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d430b00>,\n",
       " <matplotlib.lines.Line2D at 0x1925d430c50>,\n",
       " <matplotlib.lines.Line2D at 0x1925d430da0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d430ef0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d436080>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4361d0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d436320>,\n",
       " <matplotlib.lines.Line2D at 0x1925d436470>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4365c0>,\n",
       " <matplotlib.lines.Line2D at 0x1925d436710>,\n",
       " <matplotlib.lines.Line2D at 0x1925d436860>,\n",
       " <matplotlib.lines.Line2D at 0x1925d4369b0>,\n",
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     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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5ZWKCNx76E3vH+zF9Tws2E6r5+sxZRu2JxMUviLUzey833I2ku6tluMcVRGET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\ni4JmMqdyAQmVHWGbV/ckg1dPsQvbgauG4TUolw9HNpNcU2q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reef4uF4x3OeufwIfr5t7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1925c7ecba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xu,yu,'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1925dff1f98>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LgVFsnVwVItOr775iQ1SokoyuDkeIcY2txCLAygvXQrRLCI/6p7nWMq6jAqEU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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1925c7ec860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(figsize=(2,8))\n",
    "\n",
    "plt.imshow(np.flipud(zi))"
   ]
  },
  {
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}