{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!wget https://www.metoffice.gov.uk/hadobs/hadisst/data/HadISST_sst.nc.gz\n",
    "The center of Angola is located at 41°38′13″N 85°0′3″W, the intersection of U.S. 20 and State Road 127."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2018-07-10 11:10:46--  ftp://ftp.cdc.noaa.gov/Datasets/udel.airt.precip/air.mon.mean.v401.nc\n",
      "           => ‘air.mon.mean.v401.nc’\n",
      "Resolving ftp.cdc.noaa.gov... 140.172.38.117\n",
      "Connecting to ftp.cdc.noaa.gov|140.172.38.117|:21... connected.\n",
      "Logging in as anonymous ... Logged in!\n",
      "==> SYST ... done.    ==> PWD ... done.\n",
      "==> TYPE I ... done.  ==> CWD (1) /Datasets/udel.airt.precip ... done.\n",
      "==> SIZE air.mon.mean.v401.nc ... 241816997\n",
      "==> PASV ... done.    ==> RETR air.mon.mean.v401.nc ... done.\n",
      "Length: 241816997 (231M) (unauthoritative)\n",
      "\n",
      "air.mon.mean.v401.n 100%[===================>] 230.61M   498KB/s    in 5m 30s  \n",
      "\n",
      "2018-07-10 11:16:17 (715 KB/s) - ‘air.mon.mean.v401.nc’ saved [241816997]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#! wget ftp://ftp.cdc.noaa.gov/Datasets/udel.airt.precip/air.mon.mean.v401.nc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2018-07-10 11:18:37--  ftp://ftp.cdc.noaa.gov/Datasets/udel.airt.precip/air.mon.ltm.v401.nc\n",
      "           => ‘air.mon.ltm.v401.nc’\n",
      "Resolving ftp.cdc.noaa.gov... 140.172.38.117\n",
      "Connecting to ftp.cdc.noaa.gov|140.172.38.117|:21... connected.\n",
      "Logging in as anonymous ... Logged in!\n",
      "==> SYST ... done.    ==> PWD ... done.\n",
      "==> TYPE I ... done.  ==> CWD (1) /Datasets/udel.airt.precip ... done.\n",
      "==> SIZE air.mon.ltm.v401.nc ... 18669700\n",
      "==> PASV ... done.    ==> RETR air.mon.ltm.v401.nc ... done.\n",
      "Length: 18669700 (18M) (unauthoritative)\n",
      "\n",
      "air.mon.ltm.v401.nc 100%[===================>]  17.80M  1.49MB/s    in 10s     \n",
      "\n",
      "2018-07-10 11:18:48 (1.75 MB/s) - ‘air.mon.ltm.v401.nc’ saved [18669700]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#! wget ftp://ftp.cdc.noaa.gov/Datasets/udel.airt.precip/air.mon.ltm.v401.nc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from glob import glob\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from ingridlib.trend import trend\n",
    "from ingridlib.openurl import openurl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (latitude: 89, longitude: 180, time: 1974)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 ...\n",
       "  * time       (time) float32 -1271.5 -1270.5 -1269.5 -1268.5 -1267.5 ...\n",
       "  * latitude   (latitude) float32 -88.0 -86.0 -84.0 -82.0 -80.0 -78.0 -76.0 ...\n",
       "Data variables:\n",
       "    sst        (time, latitude, longitude) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  IRIDL"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCDC/.ERSST/.version3b/.sst/dods'\n",
    "ds = xr.open_dataset(url, decode_times=False, drop_variables='zlev').squeeze()\n",
    "ds = ds.rename({'T':'time','X':'longitude','Y':'latitude'})\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "#files = glob('/net/haden/home/naomi/ncep-daily/air.sig995*.nc')\n",
    "#sst_daily = xr.open_mfdataset(files)\n",
    "#files\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'sst' (time: 1974)>\n",
       "array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)\n",
       "Coordinates:\n",
       "    longitude  float32 0.0\n",
       "  * time       (time) float32 -1271.5 -1270.5 -1269.5 -1268.5 -1267.5 ...\n",
       "    latitude   float32 42.0\n",
       "Attributes:\n",
       "    pointwidth:          1.0\n",
       "    valid_max:           45.0\n",
       "    units:               degree_Celsius\n",
       "    long_name:           Extended reconstructed sea surface temperature\n",
       "    valid_min:           -3.0\n",
       "    iridl:hasSemantics:  iridl:SeaSurfaceTemperature\n",
       "    standard_name:       sea_surface_temperature"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sst_IndNE = ds.sst.sel(latitude=41.5, longitude=-85.5, method='nearest').squeeze()\n",
    "\n",
    "#sst_IndNE['time'] = pd.date_range('1854-01-01', freq='MS', periods=ds.time.size)\n",
    "sst_IndNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (lat: 73, lon: 144, nbnds: 2, time: 25756)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1948-01-01 1948-01-02 1948-01-03 ...\n",
       "  * lat        (lat) float32 90.0 87.5 85.0 82.5 80.0 77.5 75.0 72.5 70.0 ...\n",
       "  * lon        (lon) float32 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 ...\n",
       "Dimensions without coordinates: nbnds\n",
       "Data variables:\n",
       "    air        (time, lat, lon) float32 dask.array<shape=(25756, 73, 144), chunksize=(25756, 73, 144)>\n",
       "    time_bnds  (time, nbnds) float64 dask.array<shape=(25756, 2), chunksize=(25756, 2)>\n",
       "Attributes:\n",
       "    Conventions:    COARDS\n",
       "    title:          mean daily NMC reanalysis (1979)\n",
       "    description:    Data is from NMC initialized reanalysis\\n(4x/day).  These...\n",
       "    platform:       Model\n",
       "    history:        created 95/02/06 by Hoop (netCDF2.3)\\nConverted to chunke...\n",
       "    References:     http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reana...\n",
       "    dataset_title:  NCEP-NCAR Reanalysis 1"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sst_daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1900-01-01T00:00:00.000000000 to 2014-12-01T00:00:00.000000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home2/nhn2/miniconda3/envs/pangeo/lib/python3.6/site-packages/dask/array/numpy_compat.py:28: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n",
      "/home2/nhn2/miniconda3/envs/pangeo/lib/python3.6/site-packages/dask/array/numpy_compat.py:28: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    }
   ],
   "source": [
    "ds2 = openurl('air.mon.mean.v401.nc').resample(time='AS').mean(dim='time')\n",
    "nt = ds2.time.shape[0]\n",
    "ds2['time'] = np.arange(nt) \n",
    "tr=trend(ds2.air.sel(lat=slice(20,60),lon=slice(220,300)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fbee33b8208>"
      ]
     },
     "execution_count": 254,
     "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": [
    "(tr*nt).slope.plot(vmin=-1,vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbee332deb8>]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x612 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts1 = ds2.air.sel(lat=41.5, lon=275, method='nearest').squeeze()*9/5+32 \n",
    "ts2 = ds2.air.sel(lat=34, lon=242, method='nearest').squeeze()*9/5+32  # LA\n",
    "\n",
    "\n",
    "plt.figure(figsize=(11,8.5))\n",
    "ts1.resample(time='AS').mean(dim='time').plot(marker='o')\n",
    "#ts2.resample(time='AS').mean(dim='time').plot(marker='x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbee3905da0>]"
      ]
     },
     "execution_count": 211,
     "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": [
    "ts_clim = ts.groupby('time.month').mean(dim='time')\n",
    "ts_anom = ts.groupby('time.month') - ts_clim\n",
    "\n",
    "del ts_anom['month']\n",
    "#ts.sel(time=slice('2000-01-01', '2015-01-01')).plot(marker='o')\n",
    "ts1 = (ts_anom).sel(time=slice('1900-01-01', '1919-12-01'))\n",
    "tsa = (ts).sel(time=slice('1900-01-01', '1919-12-01'))\n",
    "time1 = ts_anom.time.sel(time=slice('1900-01-01', '1919-12-01'))\n",
    "(ts_anom).sel(time=slice('1900-01-01', '1919-12-01')).plot(marker='x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x612 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as dates\n",
    "import matplotlib.ticker as ticker\n",
    "plt.figure(figsize=(11,8.5))\n",
    "ax = plt.axes()\n",
    "tsa.plot(marker='o')\n",
    "tsa.resample(time='AS').mean(dim='time').plot(marker='o')\n",
    "\n",
    "\n",
    "\n",
    "ax.xaxis.set_major_locator(dates.MonthLocator(interval=12))\n",
    "ax.xaxis.set_minor_locator(dates.MonthLocator(interval=12))\n",
    "\n",
    "# Labeling the 12 months on x-axis at the positions located above\n",
    "#ax.xaxis.set_major_formatter(ticker.NullFormatter())\n",
    "#ax.xaxis.set_minor_formatter(dates.DateFormatter('%b %y'))\n",
    "\n",
    "# Centering month labels in between the ticks and removing undesirable ticks\n",
    "#xticks = ax.xaxis.get_minor_ticks()\n",
    "#for xtick in xticks:\n",
    "#    xtick.tick1line.set_markersize(0)\n",
    "#    xtick.tick2line.set_markersize(0)\n",
    "#    xtick.label1.set_horizontalalignment('center')\n",
    "plt.savefig('test.eps', format='eps', dpi=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(682639.0, 737060.0)"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error in callback <function install_repl_displayhook.<locals>.post_execute at 0x7fbfac18c6a8> (for post_execute):\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "Locator attempting to generate 1789 ticks from 682639.0 to 737060.0: exceeds Locator.MAXTICKS",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mpost_execute\u001b[0;34m()\u001b[0m\n\u001b[1;32m    147\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mpost_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m                     \u001b[0mdraw_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m             \u001b[0;31m# IPython >= 2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/_pylab_helpers.py\u001b[0m in \u001b[0;36mdraw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mf_mgr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mforce\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mf_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m                 \u001b[0mf_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m \u001b[0matexit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdestroy_all\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mdraw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2053\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2054\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2055\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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   2056\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2057\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_cursor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    431\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 433\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    434\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    435\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1473\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1474\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1475\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1476\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1477\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2605\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2607\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2608\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2609\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1188\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1190\u001b[0;31m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1191\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[1;32m   1192\u001b[0m                                                                 renderer)\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1026\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1027\u001b[0m         \u001b[0minterval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1028\u001b[0;31m         \u001b[0mtick_tups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# iter_ticks calls the locator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1029\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_smart_bounds\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtick_tups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1030\u001b[0m             \u001b[0;31m# handle inverted limits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36miter_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    975\u001b[0m                        for i, val in enumerate(majorLocs)]\n\u001b[1;32m    976\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 977\u001b[0;31m         \u001b[0mminorLocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    978\u001b[0m         \u001b[0mminorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_minor_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mminorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    979\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mminorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/dates.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1068\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1069\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1070\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1071\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1072\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/dates.py\u001b[0m in \u001b[0;36mtick_values\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1090\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdates\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[0m\n\u001b[1;32m   1091\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mdate2num\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1092\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_if_exceeds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdate2num\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1093\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1094\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_unit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36mraise_if_exceeds\u001b[0;34m(self, locs)\u001b[0m\n\u001b[1;32m   1461\u001b[0m             raise RuntimeError(\"Locator attempting to generate {} ticks from \"\n\u001b[1;32m   1462\u001b[0m                                \"{} to {}: exceeds Locator.MAXTICKS\".format(\n\u001b[0;32m-> 1463\u001b[0;31m                                    len(locs), locs[0], locs[-1]))\n\u001b[0m\u001b[1;32m   1464\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mlocs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1465\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Locator attempting to generate 1789 ticks from 682639.0 to 737060.0: exceeds Locator.MAXTICKS"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "Locator attempting to generate 1789 ticks from 682639.0 to 737060.0: exceeds Locator.MAXTICKS",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\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[0m\u001b[1;32m    239\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\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~/miniconda3/envs/pangeo/lib/python3.6/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2210\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2211\u001b[0m                     \u001b[0mdryrun\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[0;32m-> 2212\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2213\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2214\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    512\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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--> 513\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    514\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    515\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    431\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 433\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    434\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    435\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1473\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1474\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1475\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1476\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1477\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2605\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2607\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2608\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2609\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1188\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1190\u001b[0;31m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1191\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[1;32m   1192\u001b[0m                                                                 renderer)\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1026\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1027\u001b[0m         \u001b[0minterval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1028\u001b[0;31m         \u001b[0mtick_tups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# iter_ticks calls the locator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1029\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_smart_bounds\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtick_tups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1030\u001b[0m             \u001b[0;31m# handle inverted limits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36miter_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    975\u001b[0m                        for i, val in enumerate(majorLocs)]\n\u001b[1;32m    976\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 977\u001b[0;31m         \u001b[0mminorLocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    978\u001b[0m         \u001b[0mminorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_minor_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mminorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    979\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mminorLocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/dates.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1068\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1069\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1070\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1071\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1072\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/dates.py\u001b[0m in \u001b[0;36mtick_values\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1090\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdates\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[0m\n\u001b[1;32m   1091\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mdate2num\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1092\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_if_exceeds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdate2num\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1093\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1094\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_unit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pangeo/lib/python3.6/site-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36mraise_if_exceeds\u001b[0;34m(self, locs)\u001b[0m\n\u001b[1;32m   1461\u001b[0m             raise RuntimeError(\"Locator attempting to generate {} ticks from \"\n\u001b[1;32m   1462\u001b[0m                                \"{} to {}: exceeds Locator.MAXTICKS\".format(\n\u001b[0;32m-> 1463\u001b[0;31m                                    len(locs), locs[0], locs[-1]))\n\u001b[0m\u001b[1;32m   1464\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mlocs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1465\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Locator attempting to generate 1789 ticks from 682639.0 to 737060.0: exceeds Locator.MAXTICKS"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "years = mdates.YearLocator()   # every year\n",
    "months = mdates.MonthLocator()  # every month\n",
    "yearsFmt = mdates.DateFormatter('%Y')\n",
    "import matplotlib.dates as dates\n",
    "import matplotlib.ticker as ticker\n",
    "plt.figure(figsize=(20, 8))\n",
    "ax = plt.axes()\n",
    "plt.plot(time1,ts1,marker='o',linestyle='None')\n",
    "\n",
    "# format the ticks\n",
    "ax.xaxis.set_major_locator(years)\n",
    "ax.xaxis.set_major_formatter(yearsFmt)\n",
    "ax.xaxis.set_minor_locator(months)\n",
    "\n",
    "# round to nearest years...\n",
    "datemin = np.datetime64(time[0], 'Y')\n",
    "datemax = np.datetime64(time[-1], 'Y') + np.timedelta64(1, 'Y')\n",
    "ax.set_xlim(datemin, datemax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "gist_info": {
   "gist_id": null,
   "gist_url": null
  },
  "kernelspec": {
   "display_name": "Py3 pangeo",
   "language": "python",
   "name": "pangeo"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}