Plotting with illegal/discontinuous coordinates

illegal_coords.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 25, lon: 53, time: 2920)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 52.5 ...\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ...\n",
       "  * time     (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ...\n",
       "Data variables:\n",
       "    air      (time, lat, lon) float32 241.2 242.5 243.5 244.0 244.09999 ...\n",
       "Attributes:\n",
       "    Conventions:  COARDS\n",
       "    title:        4x daily NMC reanalysis (1948)\n",
       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
       "    platform:     Model\n",
       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.tutorial.load_dataset('air_temperature')\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'lon' (lon: 53)>\n",
       "array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
       "       325. , 327.5, 330. ], dtype=float32)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ...\n",
       "Attributes:\n",
       "    standard_name:  longitude\n",
       "    long_name:      Longitude\n",
       "    units:          degrees_east\n",
       "    axis:           X"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds['lon']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x3188c5828>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds['air'][0].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mess up the coordinate\n",
    "ds['lon'] = np.random.randint(200, 330, 53)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'lon' (lon: 53)>\n",
       "array([282, 303, 306, 203, 322, 215, 255, 252, 213, 311, 219, 307, 234, 234,\n",
       "       234, 227, 203, 217, 239, 215, 283, 230, 212, 232, 214, 293, 259, 234,\n",
       "       292, 297, 256, 315, 261, 281, 219, 233, 275, 244, 261, 209, 250, 233,\n",
       "       205, 259, 263, 292, 251, 314, 220, 311, 238, 286, 306])\n",
       "Coordinates:\n",
       "  * lon      (lon) int64 282 303 306 203 322 215 255 252 213 311 219 307 234 ..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds['lon']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The input coordinate is not sorted in increasing order along axis 0. This can lead to unexpected results. Consider calling the `sortby` method on the input DataArray. To plot data with categorical axes, consider using the `heatmap` function from the `seaborn` statistical plotting library.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-88ca69241b8b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'air'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Research/Computing/miniconda3/envs/sci/lib/python3.6/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    424\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    425\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 426\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_da\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    428\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mfunctools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Research/Computing/miniconda3/envs/sci/lib/python3.6/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(darray, row, col, col_wrap, ax, hue, rtol, subplot_kws, **kwargs)\u001b[0m\n\u001b[1;32m    183\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ax'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mplotfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Research/Computing/miniconda3/envs/sci/lib/python3.6/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36mnewplotfunc\u001b[0;34m(darray, x, y, figsize, size, aspect, ax, row, col, col_wrap, xincrease, yincrease, add_colorbar, add_labels, vmin, vmax, cmap, center, robust, extend, levels, infer_intervals, colors, subplot_kws, cbar_ax, cbar_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    701\u001b[0m                              \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap_params\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'vmin'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    702\u001b[0m                              \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap_params\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'vmax'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 703\u001b[0;31m                              **kwargs)\n\u001b[0m\u001b[1;32m    704\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    705\u001b[0m         \u001b[0;31m# Label the plot with metadata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Research/Computing/miniconda3/envs/sci/lib/python3.6/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36mpcolormesh\u001b[0;34m(x, y, z, ax, infer_intervals, **kwargs)\u001b[0m\n\u001b[1;32m    923\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minfer_intervals\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    924\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 925\u001b[0;31m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_infer_interval_breaks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_monotonic\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    926\u001b[0m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_infer_interval_breaks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_monotonic\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    927\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Research/Computing/miniconda3/envs/sci/lib/python3.6/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36m_infer_interval_breaks\u001b[0;34m(coord, axis, check_monotonic)\u001b[0m\n\u001b[1;32m    890\u001b[0m                          \u001b[0;34m\"the input DataArray. To plot data with categorical \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m                          \u001b[0;34m\"axes, consider using the `heatmap` function from \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 892\u001b[0;31m                          \"the `seaborn` statistical plotting library.\" % axis)\n\u001b[0m\u001b[1;32m    893\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m     \u001b[0mdeltas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.5\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoord\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: The input coordinate is not sorted in increasing order along axis 0. This can lead to unexpected results. Consider calling the `sortby` method on the input DataArray. To plot data with categorical axes, consider using the `heatmap` function from the `seaborn` statistical plotting library."
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting is prohibited\n",
    "ds['air'][0].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x318b82b70>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# still doable with matplotlib; results are crazy. \n",
    "plt.pcolormesh(ds['lon'], ds['lat'], ds['air'][0])"
   ]
  },
  {
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}