weiji14/environment.yml

## environment.yml
name: kambicestream
channels:
  - conda-forge
dependencies:
  - ipykernel=5.1.2
  - nc-time-axis=1.2.0
  - netcdf4=1.5.1.2
  - xarray=0.12.3

## var_plotter.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt  # python plotting library\n",
    "import xarray as xr  # python library for working with NetCDF n-dimensional datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcIAAAEWCAYAAAD1t5d8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3deZhcVZ3/8feHhC1CCCSAQMCgBBAY1ggIjLIoRGUbBYX5oREYWURBGRUyzkwEREFRBBUwsoVFIAQdIoohrG4hIawhLCaCSCCKkBACSCDw+f1xTtG3K1XVtztd3Z2u7+t56um6555z76kK9LfPuWeRbUIIIYRWtVJvVyCEEELoTREIQwghtLQIhCGEEFpaBMIQQggtLQJhCCGElhaBMIQQQkuLQBhCCKGlRSAMoRdIGiHpDkmvSnpM0oca5JWksyW9kF/fkaSerG8I/VkEwhB6xzXA/cBQ4OvAJEnr1sl7DHAwsB2wLbA/cGxPVDKEVhCBMIQCSV+VdENV2g8l/aAb77E5sCMwzvY/bd8AzAI+UafIGOB7tufZfgb4HvDZ7qpPCK0uAmEI7V0FjJY0BEDSQOBTwJW1Mku6SdKLdV431bnH1sATthcX0h7M6fXyP1gybwihkwb2dgVC6Etsz5f0W+BQ4KfAaOB52/fWyb9/F26zBrCoKm0RsFHJ/IuANSTJsVhwCMstWoQhLGsCcER+fwR1WoPL4WVgcFXaYGBxjby18g8GXo4gGEL3iEAYwrL+D9hW0jakgSlX18so6WZJL9d53Vyn2Gzg3ZLWLKRtl9Pr5d+uZN4QQicp/qgMYVmSfgrsQuoW3bsJ178b+D3w38BHgMuAkbb/USPvccBJwIcAA1OBH9q+qLvrFUIrihZhCLVNAP6F7u8WrTgMGAUsBM4CDqkEQUn/KunlQt6fAL8kjSx9GPhVTgshdINoEYZQg6RNgMeAd9p+qbfrE0JonmgRhlBF0krAycC1EQRD6P8iEIZQIOkdwEvAh4FxvVydEHqcpAGS7q/Mg5W0vaS7JT0gaaaknQt5x0qaK+lxSfv1Xq2XT8wjDKHA9iukeXshtKqTgEdpm7LzHeA02zdL+mg+3lPSVqRn3VsDGwK3Strc9pu9UenlES3CEEIIAEgaDnwMuLiQbNqC4lrAs/n9QaTHB0tsPwnMBXZmBRQtwm42UKt4FQYBsMGmG/KONVbttmu/8vKSbrtWCKH3PfHko8/brrfYeimbrT3Ar75RbtDj/Fc8G3itkDTe9vjC8Q+ArwHFOa5fAqZIOofUeNotp28E3F3IN4/6qyP1aREIu9kqDGIz9gDgJ6Pv6uXahBD6st0u5Knlvcarb5hjti/3B/dpf3jtNdujap2TtD/wnO17Je1ZOHU88GXbN0j6JHAJaU5rra3AVshpCBEIQwihh73/grya3oV9alvJ3YED83PA1YDBkq4CDiA9NwS4nrZu03nAxoXyw2nrNl2hxDPCJpry0NJ2rxBCeDsI9jG2x9oebnsEaRDM7baPIAW3D+ZsewNz8vvJwGGSVpW0KTASmNHD1e4W0SLsQcVguN+28dWHEFYInwPOy1uSvUbaKBrbsyVNBB4BlgInrIgjRiECYQghhCq27wTuzO9/D+xUJ9+ZwJk9VrEmia7REEIILS0CYQghhJYWgTCEEEJLi0AYQgihpUUg7AUxYjSEEPqOCIQ9LIJgCCH0LfFbuYdEAAwhhL4pfjs3UQS/EELo+6JrNIQQQkuLQBhCCKGl9ZlAKOlSSc9JerjGua9IsqRhhbSxkuZKelzSfoX0nSTNyufOl6Scvqqk63L6dEkjCmXGSJqTX2MK6ZvmvHNy2VWa9flDCCH0jj4TCIHLgdHViZI2Bj4M/LWQthVpdfStc5kLJA3Ipy8kLQo7Mr8q1zwaWGh7M+Bc4Ox8rXWAccAupN2Vx0laO5c5GzjX9khgYb5GCCGEfqTPBELbvwUW1Dh1LmnH5OKGjwcB19peYvtJYC6ws6QNgMG2p9k2cAVwcKHMhPx+ErBPbi3uB0y1vcD2QmAqMDqf2zvnJZetXCuEEEI/0WcCYS2SDgSesf1g1amNgKcLx/Ny2kb5fXV6uzK2lwKLgKENrjUUeDHnrb5WdT2PkTRT0sylvN6pzxhCCKF39dnx/ZIGAV8H9q11ukaaG6R3pUyja7VPtMcD4wEGaUjNPCGEEPqmvtwifA+wKfCgpL8Aw4H7JL2T1DrbuJB3OGkX5Xn5fXU6xTJ5g8m1SF2x9a71PDAk562+VgghhH6izwZC27Nsr2d7hO0RpIC1o+2/AZOBw/JI0E1Jg2Jm2J4PLJa0a37G9xngxnzJyUBlROghwO35OeIUYF9Ja+dBMvsCU/K5O3JectnKtUIIIfQTfSYQSroGmAZsIWmepLojNG3PBiYCjwC/AU6w/WY+fTxwMWkAzZ+Bm3P6JcBQSXOBk4FT87UWAGcA9+TX6TkN4BTg5FxmaL5GCCGEfqTPPCO0fXgH50dUHZ8JnFkj30xgmxrprwGH1rn2pcClNdKfIE2pCCGElpCnos0kDVTcX9J3gQOA10mNiyNtv5jzjiVNK3sTONH2lF6q9nLpMy3CEEIIfcJJwKOF46nANra3Bf4EjIUO53OvUCIQhhBCD5r2+TV7uwp1SRoOfIz0eAkA27cUppHdTduAxJrzuXuyvt0lAmEIIYSKH5AWMHmrzvmjaBt3UW8O9gqnzzwjDCGE0HlrDVLpLd9O+wPDJM0sJI3P86CRtD/wnO17Je1ZXVbS14GlwNWVpBq3WCHnUUcgDCGE1vG87VF1zu0OHCjpo8BqwGBJV9k+Im9GsD+wT55aBvXnYK9woms0hBB62OuTGw6S7xW2x9oenkfoH0aaa32EpNGkqWQH2n61UKTmfO4er3g3iEAYQgg97N7f3NTbVeiMHwFrAlMlPSDpIuhwPvcKJbpGQwghtGP7TuDO/H6zBvlqzude0USLMIQQekFfnkbRaiIQhhBCaGkRCEMIIbS0eEbYw3YavX9vVyGE0EtWOfCa9gkX1pqKF3paBMImiqAXQih6ffLhywbD0OuiazSEEEJLi0AYQgihpUUgDCGE0NIiEIYQQg/qi8ur9VWSVpe0RbPvE4EwhBBCnyPpAOAB0vJtSNpe0uRm3CsCYQgh9LBoFZbyDdJGvy8C2H4AGNGMG/WZQCjpUknPSXq4kPZdSY9JekjSLyQNKZwbK2mupMcl7VdI30nSrHzufEnK6atKui6nT5c0olBmjKQ5+TWmkL5pzjsnl12l2d9DCCEEAJbaXtQTN+ozgRC4HBhdlTYV2Mb2tsCfgLEAkrYibROydS5zgaQBucyFwDGkLUFGFq55NLAwLyB7LnB2vtY6wDhgF9JfH+MkrZ3LnA2ca3sksDBfI4QQltub86f3dhX6uocl/TswQNJIST8E/tiMG/WZQGj7t8CCqrRbbC/Nh3eTNn4EOAi41vYS208Cc4GdJW0ADLY9LW8eeQVwcKHMhPx+ErBPbi3uB0y1vcD2QlLwHZ3P7Z3zkstWrhVCCMvlzXt+0NtV6Ou+SGrsLAF+BiwCvtSMG5VaWUbSRsC7ivlz4OpJRwHX5fcbkQJjxbyc9kZ+X51eKfM0gO2lkhYBQ4vpVWWGAi8WAnHxWu1IOobUCmVlVu/CRwshhFCUNwH+en41VYeBUNLZwKdImy9WNl000GOBUNLXgaXA1ZWkGtncIL0rZRpdq32iPR4YDzBIQ2rmCSGEUJ6kqcChtl/Mx2uTegL3a1yy88q0CA8GtrC9pLtvXkYevLI/sE/u7oTUOtu4kG048GxOH14jvVhmnqSBwFqkrth5wJ5VZe4EngeGSBqYW4XFa4UQQmiuYZUgCGB7oaT1mnGjMs8InwBWbsbNOyJpNHAKcGBuJldMBg7LI0E3JQ2KmWF7PrBY0q75Gd9ngBsLZSojQg8Bbs+BdQqwr6S1818c+wJT8rk7cl5y2cq1QgghNNdbkjapHEh6F3V65ZZXmRbhq8ADkm4jPbQEwPaJ3VkRSdeQWmbDJM0jjeQcC6wKTM2zIO62fZzt2ZImkrprlwIn2K502x5PGoG6OnBzfgFcAlwpaS6pJXhY/hwLJJ0B3JPznW67MmjnFOBaSd8E7s/X6BED3teUZ8IhhF4yYINdaqRe2+P1WIF8Hfi9pLvy8QfIYzG6m9p6G+tkKMyrK7I9oVZ6qxukId6MPQC4/9n/6eXahBD6ilqBUNK9tkctz3Xfu94AX3rIoFJ5d7vw5Yb3k7QaafzHqqSG0iTb4/K5LwJfIDU+fmX7azl9LGlq2ZvAibanVF1zFrVbcgKcp8fVq88wYNecd5rt50t90E7qsEUYAS+EEJbfm/On12kV9ilLgL1tvyxpZVKL7GZSD9tBwLa2l1Se1VXN6d4QuFXS5oUeOkhjPEqTtKXtxyTtmJMqYzM2kbSJ7fu6/vFqKzNqdCTwbWArYLVKuu13d3dlQggh9J48NuLlfLhyfpn0yOmsyqBJ28/lPG/P6QaezI+edgamFa75VCercTKpC/R7tapImt/drco8I7yM9LzuXGAv4EhqTy0IIYTQwwYNXoudRpecUXDhtcMkzSykjM/Tv96WV+m6F9gM+LHt6ZI2B/5V0pnAa8BXbN9D/Tndy5C0K/BD4L3AKsAA4BXbg4v5bB+Tf+5V7kMtvzKBcHXbt0lSjuzfkPQ7UnAMIYRQUh/oHn2+o2eSuVtz+7y28y8kbUOKFWuTnte9D5go6d10Yr418CNSN+r1wCjSqP7NuvQpulmZQPiapJWAOZK+ADwDNGUuRwghhL7B9ouS7iSt1zwP+HnuOp0h6S1gGPXndNe75lxJA3KwvUxSU9YO7awy8wi/BAwCTgR2Aj5N23y8EEIIndCXF9uWtG5llx9JqwMfAh4D/o/8bC53k65CWnSk5pzuOpd/Ne/g84Ck70j6MvCOGnXYPf9ctVs/XANlRo1W5te9THo+GEIIYTn0gS7SejYAJuTnhCsBE23flAPYpXmbvNeBMbl12GhOd7VP52t+AfgyqSX58Rr5zic1uqYBO9Y43+3qBkJJP7D9JUm/pEafr+0Dm1qzEELox/piy9D2Q8AONdJfB46oU+ZM4MwSlz/Y9nmkwTanAUg6CTivKt8bki4DNpJ0fo37detiLtC4RXhl/nlOd980hBBCyxnDskHvszXS9id1ye5NGr3adHUDoe1788/K8jaV1b83zn81hA48s3Tz3q5CCKGP2GTjtTvO1A9JOhz4d2BTSZMLp9YEXqjOn1ePuVbSo7Yf7Ik6lplQfydwYM77APAPSXfZPrnJdQshhH7jr08vbNVg+EdgPmmUaXGS/GKgUaPqBUm/AHYnPZ77PXCS7XkNynRJmekTa9l+SdJ/AJfZHicpWoQhhNBJrRgM8/zzp4D3d7LoZaSd6Q/Nx0fktA93X+2SMtMnBkraAPgkcFN3VyCEEFrJX59e2NtV6BWSPi5pjqRFkl6StFjSSw2KrGf7MttL8+tyYN1m1K1MIDydtGffn23fk1cTmNOMyoQQQito0WD4HdLesmvZHmx7zerl1ar8Q9IRkgbk1xHUeKbYHcrMI7yetCRO5fgJ4BPNqEwIIbSKFgyGf7f9aCfyH0Valu1c0jPCP+a0bldmsMzmwIXA+ra3kbQtKap/sxkVCiGE0C/NlHQdaZWa4ibvP6+V2fZfSQM1m67MYJmfAl8FfgJpwqWknwERCLvBjLue7O0qhBCaYOqVM/jJlON6uxp9yWDgVWDfQpqBmoGwJ5UJhINsz5DaLTK+tEn16VciyIXQuj786Z17uwp9iu0+u0RnmcEyz0t6D3mZNUmHkOaEhBBCaGDSVd2+mfoKS9JwSb+Q9Jykv0u6QdLw3q4XlAuEJ5C6RbeU9AxpN4po74cQQuiMy0i7VWxI2rz3lzmtJknrS7pE0s35eCtJRzejYmUCoW1/iDR/Y0vbe5Qs1ymSLs1/KTxcSFtH0tQ892RqXuKtcm6spLmSHpe0XyF9J0mz8rnzlft08zYh1+X06ZJGFMqMyfeYI2lMIX3TnHdOLrtKd3/uEEJoEet2cl7g5aSpexvm4z+RGmLdrkxAuwHA9iu2F+e0SU2oy+WkDSCLTgVusz0SuC0fI2kr0k7HW+cyF+RtQyCNcD2GtC/WyMI1jwYW2t6MNBz37HytdYBxwC7AzsC4QsA9Gzg3339hvkYIIZQW3aNve76T8wKH2Z4IvAVgeylQb4un5VI3EEraUtIngLXyigCV12eB1bq7IrZ/CyyoSj4ImJDfTwAOLqRfa3uJ7SeBucDOeQWcwban5b2yrqgqU7nWJGCf3FrcD5hqe4HthcBUYHQ+tzdtQb94/xBCCJ1zFGmFsr+RxpkcQuN5ga9IGkrb+JRdgUXNqFijUaNbkLbDGAIcUEhfDHyuGZWpYX3b8wFsz5e0Xk7fCLi7kG9eTnsjv69Or5R5Ol9rqaRFwNBielWZocCL+a+Q6mu1I+kYUiuUlVm9858yhNCvTbrqPg45okf2mO3LXL2PraR3Nsh/MumZ4nsk/YHUjXpIMyrWaBumGyXdBJxi+1vNuPlyUI00N0jvSplG12qfaI8HxgMM0pCaeUIIocU9Kel64Cjb/8xpv6bOLvS275P0QVKjTMDjtt9oRsUaPiO0/SZNWOm7E/6euzvJP5/L6fOAjQv5hgPP5vThNdLblZE0EFiL1BVb71rPA0Ny3uprhRBCp8SzQmYBvwN+n6fkQe0GRzohHQqsbns26bHUdZKa0qwuM1jmj5J+JOlfJe1YeTWjMjVMJu1qTP55YyH9sDwSdFPSoJgZuRt1saRd8zO+z1SVqVzrEOD2/BxxCrCvpLXzIJl9gSn53B20NcWL9w8hhE6bdNV9fTogSlpN0gxJD0qaLem0nN7pEfw12PYFwInALyUdQJ1etux/bC+WtAdpLMcE0mDIblcmEO5GGp15OmlTxe8B53R3RSRdA0wDtpA0L88XOQv4sKQ5pJbpWQD5L4SJwCPAb4ATcusV4HjgYtIAmj8DN+f0S4ChkuaS+p5PzddaAJwB3JNfp+c0gFOAk3OZofkaIYSwXPpwMFwC7G17O2B70sDBXenaCP5qArD9B2Af0tKdWzaoS+V3+seAC23fCDRlCluZ3Sf2asaNa9zn8Dqn9qmT/0zgzBrpM4FtaqS/RtsGj9XnLgUurZH+BGlKRQghdKu+GAxzT9jL+XDl/DJp1P2eOX0CcCepofD2CH7SM8C5pN+Z02pc/qOF+8yXtDepoVXPM5J+AnwIOFvSqjRhDjuUW2sUSR8jRfy3p03YPr0ZFepPpl45o7erEELoIV1aYPvTy39fvWN9Bryv7Dzza4dJmllIGJ8H+7VdL7Xo7gU2A35se7qkzo7gL17vCNtXAYdLNR8J/rZOZT9JamWeY/vFPE7kq6U+ZieV2YbpImAQsBepy/EQYJnf8JIeKnG/f9iu2cILIYQV2bH7XbQi7DbxvO1RjTLkx0zbSxoC/ELSMj1sBWVG178j/1yzfDXB9qvAzyWtJ2mTnPxYZ65RVpkW4W62t5X0kO3TJH2P2ttmDKDQ9K1BpAErIYTQLx2730VAF1uHfUxuhd1JapX9XdIGuTVYZgR/8To/ya3Ml2yfW/b+kg4kjUnZMN9vE1Ig3LqQZ50Sl3rL9ouNMpQJhJX5Hq9K2pC0JM6mNfIda/upRheS9PkS9wshhBVaJSDCihUUJa0LvJGD4Ork53O0jbo/i2VH8P9M0vdJAWskNXoMbb+ZA1vpQEgaxLgrcKvtHSTtBVSPJXk2v+pOwyA10jZpcL5UILwpN5G/C9xHavZeXJ3J9u87ulCZPCGE0J8Ug2JRHw2QGwATcgtuJWCi7ZskTQMm5tH8fyUPPLQ9W1JlBP9S2o/gr/ZHST8CrgNeqSTarjdq6A3bL0haSdJKtu+QdHZVnkdt79DoA0m6v/FHLjdq9Iz89oa80sxqtuuu9yZpf1Ikf1e+vtJlPLije4UQQquoFyB7k+2HgGUCi+0X6OQI/hoqI0SLAy1NWtO5lhclrUGahH+1pOdYdlP495e4b4d56gbCvDK4bF9ZSbO9RNJnJL1i+2d1iv4A+DgwKw/FbVnTbrmJh3xTb1cjhNBHjdfxvV2FHtOFqXgHAa+Rtl76f6TVwNrNVsjT4mqStIbtlxvlqWg0J+M/gf+rkX5tPlfP08DDrR4EQwghtJG0lqTvS5qZX9+TtFa9/LZfIS20/VHScpgTc8u0rEfKZmzUNTqgsP9gsXKLJa3coNzXgF9Luou0SkGl3PfLViqEEEK/cynwMGl+IKRZlJeRehCXIek/gP8Fbic9YvuhpNPzAiiVPCfXuZeANcpWrFEgXFnSO3JULlZuTRovc3MmaWWC1TrIF0IIoXW8x/YnCsenSXqgQf6vAjtUWoF5b8I/0n4VsG+RBnJWPzuETqxC0ygQXgJMknS87b/kiowAfkzjNTfXsb1v2QqEEEJoCf+UtEdl9oCk3WmbnlfLPNL+txWLab93LKSZDP9n+97qwrlFWUqj/QjPkfQycFceuWPSkNezbDdaAfxWSfvavqVsJUIIIfR7xwFX5OeCIj33+2x1pkJ35zPAdEk30rbeafUcxSNJc9trabiCTlHD6RO2LwIuyoFQtZ4Z1nAC8DVJS0g7xsf0iRBCaHG2HwS2kzQ4H79UJ2tlKbY/51fFMtvg2X68Ok3SO23/zfbfy9at1KLbtl/uONfbeTu1nlwIIYT+L+8e8QlgBDCwsgB39QYOtk9bzlvV3fW+nm7b0kLSO7sjTwghhH7pRlL35lLSY7bKq7s1Wm6tplItwpLKROFOR+oQQgj9wnDbo3vgPj/tbIGy+xHuRm7OVtJsX1GVbTtJ9fp8IUXpRudDCCH0X3+U9C+2Z5XJLGn3vJt9R2krAQ/Z3gbA9gWdrViZ/QivBN4DPABUFlM10C4Q2h7Q2ZuHEELo3yTNIsWMgcCRkp4gLbZSGUi5bZ2iP2TZHsRl0my/JelBSZvY/mtX6limRTgK2CqWTAshhNAF+3cms6T3kxboXrdq5ZjBpC2VatkAmC1pBu13tjiwzD3LBMKHgXcC88tcsBkkfRn4D9JfFbNIc0cGkbbzGAH8Bfik7YU5/1jgaFIL9kTbU3L6TsDlwOqk55Un2XYezXQFsBNpTsqnCosIjAH+O1flm7YnNPfThhBC/9HRPrU1rEJaHm0g7Xe1fwk4pE6Z5RppWiYQDgMeyZG2uHZoqUi7vCRtBJxIapX+M+99dRiwFXCb7bMknQqcCpwiaat8fmvSRpG3Sto875F1IXAMcDcpEI4GbiYFzYW2N5N0GGkjyk/l3Y/HkVrFBu6VNLkScEMIIXQv23eRFnK5vGwQzWW6rEwg/EZnLyppbWBj2g+uqbf5YhkDgdUlvUFqCT4LjAX2zOcnAHcCp5CG515rewnwpKS5wM6S/gIMtj0t1/EK4GBSIDyIts85CfiR0iSX/YCpthfkMlNJwfOa5fgsIYTQMiStmn8fd9arkr5LatSsVkm0vcz+hZJ2JT0/fC+pRTkAeKXsQi5lNubtVKSVdAZp2Zw/k1pR0HjzxY7u/4ykc0i7Iv8TuMX2LZLWtz0/55kvab1cZCNSi69iXk57I7+vTq+UeTpfa6mkRcDQYnqNMsXPfAyppcnKrN6VjxlCCP3VNGBHSVfa/nQnyl1Nevy1P2l5tjHAP+rk/RGpJ/B6Ug/eZ4CRZW/UaGPe39veQ9Ji2gIadLxk2idJq4y/XrYSjeTW5UHApsCLwPV50+C6RWqkuUF6V8u0JdjjgfEAgzQkBhWFEEKbVfJYi90kLbPlku2f1yk31PYlkk4qdJfWbZjZnitpQH4MdpmkP5atYN2VZWzvkX+uaXtw4bVmB83Nh4EhZStQwoeAJ23/w/YbwM9JI4r+LmkDgPzzuZx/HqlbtmI4qSt1Xn5fnd6ujKSBpJ2QFzS4Vggh9DuSNpZ0h6RHJc2WdFLV+a9IsqRhhbSxkuZKelzSfjUuexywKykuHFD1ajSi9I38c76kj0nagfa/w4telbQK8ICk7+QBlu8o85mhe1eWqfg2cL+kh+mewTV/BXaVNIjUNboPMJM0RHYMcFb+WVmQdTLwM0nfJw2WGQnMsP2mpMW5L3k6qen8w0KZMaQm/CHA7Xk06RTgW7lVCrAv6dlkCCH0R0uB/7R9X9579l5JU20/Imlj4MOk38kAdDA4EYC87dLvJc203WgLv2rfzDtV/Cfpd/Vg4Mt18n6a1LD7Qs6zMWld01KaEQgnkEZdzgLeWt6L2Z4uaRJp36mlwP2kbsg1gImSjib9wxya88/OI0sfyflPKPyjHE/b9Imb8wvS/opX5oE1C0j/sNhekJ953pPznV4ZOBNCCP1NHndRGXuxWNKjpHERjwDnAl+j/S4QNQcnkhoV1a6UdCLwgXx8F3BR7umrVZeb8ttFwF4d1PspSasDG3Rl0e5mBMLnbZ/fnRe0PY40jaFoCal1WCv/mcCZNdJnAtvUSH+NHEhrnLuU9jsihxDCimqYpJmF4/F5jMMy8kbsO5D2BDwQeMb2g5VdI7J6gxNruQBYOf+E1Iq7kDRHfLlIOgA4hzRidFNJ25MaLt02oR5J7wJG2r41R92BDfYmvFfSt0ndjcWu0eWZPhFCCKGG170azyzdvGz25213uGFt3oP2BuBLpJ61r5MeDS2TtUZavQGD77O9XeH4dkkPdlSXkr5BaoneCWD7gRzISymz1ujnSFMD1iGtOTocuIg6rTHSXxCQHo5WdHn6RAghhJ4jaWVSELza9s8l/Qtp1H6lNTgcuE/SznRuQOGbkt5j+8/5Pu+mbf3q5bXU9qKq1mppZVqEJ5Ai7XQA23MKc/aWYbthX24IIYS+KS8kcgnwqO3vA+TdItYr5PkLMMr285JqDk6sc/mvAnfkRbcFvIu0XGa9uqwPfAvY0PZH8sCc99cZcPOwpH8HBkgaSVqNbPmnTxQsKc4JzNML6s6Vk7S+pEsk3ZyPt8oDWkIIIfRtu5Oe3e0t6YH8+mi9zLZnA5XBib+h/eDE6ry3kQLlifm1he07GtTlcmAKKcAC/InUVVvLF0kjV5cAPyMNsKmXdxllWoR3Sfov0hJnHwY+D/yyQf7LgctIfcqQKibqFs8AABsbSURBVH8d6a+MEEIIfVSe6tCwf9H2iKrjmoMT65RdAjxUsjrDbE9U2kShsupXva7Ud9v+Om1xp1PKtAhPJS1rMws4Fvh1vmE9w2xPJE+dsL2U7usHDiGE0BpekTSU3AOZ54AvqpP3IkkzJH1eUqcXdCnTIvyi7fOAn1YS8pI359XJ35nKhxBCCLWcTJp98B5JfwDWpc42THk50M1JzxxnKu2WdJntqWVuVKZFOKZG2mcb5K+u/BWk/uAQQggtSskRkv43H2+SR57WlKfcfZC0pOaxwNa263ar2v4Tae/YU3K58yU9Vmt902qNFt0+HPh30uTEyYVTa5I2r61ndq7EFqS+5scpF3BDCCH0XxeQHpntDZwOLCZN03hfrcySTiBN4Zidj9eWdLjtC2rk3ZbUGvwYMBU4IC8TtyFplZt6C3sDjbtG/0haamcY8L1C+mIaP+ycZntHUkCsVPI+YMdGFQkhhNCv7WJ7R0n3A9hemBfKrudztn9cOcj5P0fbyjRFPyI9vvsv2/8slHlW0n93VLG6gTDvDPwU8P6OLgIg6Z2kpXVWz6uEV0YeDSZtphtCCKF1vSFpAG3jR9al8XrUK0mS7Ur+AaQl1JZh+wO10vO5KzuqWJmVZcru/Lsf6dnhcFILshIIFwP/1dF9Qggh9GvnA78A1pN0JmngS6PW2hTSxgoXkYLncaS5it2uzKjRWjv/bladyfYEYIKkT9i+oVtrGUIIYYVm+2pJ95KW5xRwsO1HGxQ5hTRI5vic/xbg4mbUrdSi253c+Xe4pMGkluBPSc8GT7V9y/JXN4QQwopE0jqFw+eAa4rn6m1tZ/st0u4UFza3huUCYbudf0kDaBrt/HuU7fOUdipejzSS5zJSNA8hhNBa7iV1bQrYBFiY3w8h7SW7aTGzpIm2PylpFjWW87S9bXWapFGkVWXeRYprSlmXzVtLmUDY2Z1/K88GP0qa0PigurokeAghhBWa7U0B8rO+ybZ/nY8/AnyoRpGT8s/9O3Gbq0mLendpQ/gOA2EePQrwGlBm5997Jd1CivJjJa3ZlYqFEELoV95n+7jKge2bJZ1Rncn2/PzzqepzDfzD9uSOs9VWZtTo7qRNDytNTgBsv7tOkaOB7YEnbL+al1uru9VGCCGElvB8ntN3FanL8wgaL87SGeMkXQzcRvsN4RtOpK8o0zV6CalL9F7KLZ49kfRM8IFckRfovg8bQghhxXQ4MI40hQLgtzmtOxwJbAmsTFsPpOlgRZmKMkufLbJ9s+3nbL9QeTXIfxFpabY5ks6StGWZijQiaYikSXnduEclvV/SOpKmSpqTf65dyD9W0lxJj+dBO5X0nSTNyufOrzy7lLSqpOty+nRJIwplxuR7zJFUa93VEEIIHbC9wPZJtnfIr5NqjRiVdFv+eXYnLr+d7VG2x9g+Mr+OKlu4TIvwDknfJUXWYpPzvlqZbd8K3CppLVK0nyrpadJUiqtsv1G2cgXnAb+xfUgewTqINEn/NttnSTqVtF3UKXkX48NImzRumOuyeZ76cSFwDHA38GtgNHAzqTt3oe3NJB0GnA18Kg/7HUeaP2nS88/Jthd24TOEEELLknQHtUeB7l2VtIGkDwIHSrqWqv0R68SeuyVtZfuRrtStTCDcJf8cVawLaeHUmvJzwSNII07vJ43o2YO0k8WenalgnpP4AfKOF7ZfB16XdFDhWhOAO0kTMA8Crs0bQD4paS6ws6S/AINtT8vXvQI4mBQIDyI9BwWYBPwotxb3A6ZW/mqRNJUUPN+eBxNCCKGUrxTer0aafbC0Rr7/JTVshgPfrzpXL/bsAYyR9CSpwda90yds71XmQhWSfk7qq72StAL4/HzqOkkzO3Ot7N2kjYEvk7Qd6VnlScD6hdFF8yWtl/NvRGrxVczLaW/k99XplTJP52stlbQIGFpMr1EmhBBCSbbvrUr6g6S7auSbBEyS9D+2lxlVWsfo5albo22YjrB9laSTa523XR2pK35k+/Y6ZUbVSi9Rxx1JGwRPl3Qe6a+FemrNWXSD9K6WabuhdAypy5WVWb1B1UIIoe+SdClp/t5ztrfJaduTxn6sRmrBfd72jHxuLOnR0pvAibanNLh2cYWZlYCdgHfWy2/7DEkHknoEAe60fVOdvJ2ZarGMRoNlKqvHrFnnVc/QPHcQSf8t6eeSlmcLpnnAPNvT8/EkUmD8u6QN8n02IC3dU8m/caH8cODZnD68Rnq7MpIGAmsBCxpcqx3b4/OD2lEDay+OHkIIK4LLWbZ19R3gNNvbk7otvwNQNR5jNHBB3iGinnuBmfnnNOA/SUG0JknfJvX+PZJfJ+W0Yp6aY1U6m6fRNkw/yT/LTKIv+h/b10vag/SM7RzSIJVdGherW4+/SXpa0ha2Hyct2Fr5YsYAZ+WfN+Yik4GfSfo+abDMSGCG7TclLc67aUwnLR7+w0KZMaR/nEOA221b0hTgW4URqfsCY7vyOUIIoa+z/dviqPlKMmk7PUiNhEpjoOZ4DNLv0Vrea/u1YoKkVRtU52PA9nnNUSRNII05Kf4Ofq+kRvvjKte5oUZdo+c3Kmj7xDqnKnMNPwZcaPtGSd/oqCId+CJwdR4x+gRpzshKpC06jiatV3dortdsSRNJgXIpcEIeMQppFfPLgdVJg2RuzumXAFfmf8gFpL9ysL0gr3xwT853er0FYkMIYQUwrGqsxnjb4zso8yVgiqRzSL93d8vp9cZj1PNHlt2gfVqNtKIhpN/JUDuglZme1+H890aDZSoPNncHtgKuy8eHFs7V8oykn5DWkDs7R/wy8xXrsv0A7UetVuxTJ/+ZwJk10mcC29RIf40cSGucuxS4tDP1DSGEnvLKy0uYcdeTZbM/34WxGscDX7Z9g6RPkhoOH6L8GIqubtr+beD+PO1CpGeF7XrklvfZYEWjrtEJAJI+C+xVmf+XF05ttJPEJ0n9xefYfjE/v/tqd1Q2hBBCjxtD20LY19O2J2CpMRS037S9OMiy4abttq+RdCfwPlIgPMX23zpf/Y6VmUe4IWlwTKV5ukZOq8n2qxSWtclTHObXyx9CCKFPexb4IGmu9t7AnJxeczxGdeHl2bQ9x48uL6ZdVplAeBZtzVNIX8g3mlajEEIIvULSNaSFSoZJmkdaWetzwHl5RP1r5KliHYzHKF7zCNtXASNqTcdrMBWvxzQMhHl1lVtJg0oqoz5PbVbzNIQQQu+xXW8R7J3q5K85HqNKZSreGl2tV7M1DIR5CsH/2d6JtukJIYQQQildnYqXR6leZnt2UypWUKZr9G5J77N9T8dZQwghhGVJWpfUzTqC9nvb1tsl4jFgfO6SvQy4xvaiZtStTCDcCzhW0lPAK3RyMdMQQgiB1Kv4O9Ljtg7n9tm+GLhY0hakueMPSfoD8FPbdzQu3TllAuFHuvOGIYQQWtIg26d0pkBesm3L/HoeeBA4WdKxtg/rrop1ONE9T1gcAhyQX0O6axJjCCGElnGTpI+WzZynZTwGfBT4lu2dbJ9t+wBgh+6sWIeBUNJJpP0E18uvqyR9sTsrEUIIod87iRQM/ynppbz280sN8j9M2nn+2MpuFwU7d2fFynSNHg3sYvsVAElnk9aH+2HDUiGEEEJmu9GuRbX8v7zE5dsk3WZ7n+4eNFMmEIr2DzbfpPYacyGEEEJNdbbjWwQ8ZXtpId9qpDVIh+Wdf4prk9Zd1Wx5lAmElwHTJf0iV+gg0qKrIYQQQlkXkHaamJWP/4U0+GWopONsV9awPpa048WGQHEvwZeAHzejYh0GQtvfzwuf7pGTjrR9fzMqE0IIod/6C3B0ZYJ83tj3q8AZpPWpbwGwfR5pSbcv2u6RR3BlWoSQukOdX281rzohhBD6qS2Lq8TYfkTSDrafSKt5JpL2tn07aUu/j1dfxPbPq9OWV4eBMI8a/RxwA6lr9CpJ43sqUocQQugXHpd0IXBtPv4U8Ke8Z+0bhXwfBG4nTderZgq7G3WXGDUaQgihJ3wW+Dzp+Z+A3wNfIQXBvSqZbI/LP4/sqYqV2Tk+Ro2GEEJYLrb/aft7tv/N9sG2z7H9qu23bL9cnV/StyQNKRyvLembzahbmUBYGTX6DUnfAO4mRo2GEELoBEkjJU2S9IikJyqvBkU+YvvFyoHthaRVZrpdZ0eNihg1GkIIofMuI230ey6pK/RIGvcuDpC0qu0lAJJWB1ZtRsXKLLG2KzDH9vl5WOtcSbt0VK67SRog6X5JN+XjdSRNlTQn/1y7kHespLmSHpe0XyF9J0mz8rnz88bDSFpV0nU5fbqkEYUyY/I95kga03OfOIQQ+pXVbd8GyPZTtr8B7N0g/1XAbZKOlnQUMBWY0IyKlekavRAo9t++ktN62knAo4XjU4HbbI8EbsvHlbkphwFbA6OBC/IK5pDqfQwwMr9G5/SjgYW2NyP9tXJ2vtY6pL9gdiGtbTeuGHBDCCGU9pqklYA5kr4g6d9I61fXZPs7wDeB9wJbAWfktG5XarCMbRcq9xbl5x92C0nDgY8BFxeSD6Ltr4MJwMGF9GttL7H9JDAX2FnSBsBg29Py57miqkzlWpOAfXJrcT9gqu0FuX96Km3BM4QQ+hVJl0p6TtLDhbTvSnpM0kOSflE1gKVm71sdXyItnXYisBPwaaCjXrb7gbuAO/P7pigTCJ+QdKKklfPrJKDRA85m+AHwNdpP5l/f9nyA/LPyl8VGwNOFfPNy2kb5fXV6uzJ5zbtFwNAG12pH0jGSZkqauZTXu/L5QgihL7icZf/Ynwpskzdj/xMwFjrsfVuG7Xtsv2x7nu0jbX/c9t318kv6JDADOAT4JGnQ5iFd/2j1lQmExwG7Ac+QAsEupO7FHiFpf+A52/eWLVIjzQ3Su1qmLcEeb3uU7VEDWaVkNUMIoW+x/VtgQVXaLYVFse8Ghuf3NXvf6l1b0qjcorwvty4fkvRQg+p8HXif7TG2P5Ov/T9d/GgNlRk1+hwp6veW3YED84aOqwGDJV0F/F3SBrbn527P53L+ecDGhfLDgWdz+vAa6cUy8yQNBNYi/ccwD9izqsyd3ffRQgihRw2TNLNwPN72+E6UPwq4Lr/fiBQYK2r2mBVcTVpbdBbllupcKcefihco13jrtE4965N0n+1aW2k0je2xtDXF9wS+YvsISd8l9S+flX/emItMBn6WdzfekDQoZobtN/NGkLsC04HP0LY6zuR8jWmkZvjtti1pCvCtwgCZfSt1CSGEvuClF15h6pXV+9bW9bztUV25j6SvA0tJAQ1K9pgV/MP25E7c8jf5d/A1+fhTwK87Ub60zg566UsrypwFTJR0NPBX4FAA27MlTQQeIf2jnWC7sjLO8aQ+8NWBm/ML0gIBV0qaS2oJHpavtUDSGcA9Od/pttt1G4QQQn+Xp47tD+xTGDxZr/etnnGSLiaN8l9SSay3iLbtr0r6BKlXUKTW6y+6/inq62wg/FUzKlGW7TvJXZO2XwD2qZPvTODMGukzgW1qpL9GDqQ1zl0KXFrrXAgh9HeSRgOnAB+0/WrhVM3etwaXOhLYEliZtq7Rhoto276BtOFDU9UNhJI2tl0cMYnt/87n/tX275pduRBCCD1H0jWkcRHDJM0jzaMeS1rRZWpeg+Ru28d10PtWy3a2/6VEHRZTu4tVgG0P7sxnKqNRi/AuSRcB36+MGJK0PvA9YAvgfd1dmRBCCL3H9uE1kuuuLV2v962OuyVtZfuRDuqwZsnrdZtGI3B2At4D3C9p7zx/cAZpQEmPL7EWQghhhbYH8ECefP9QXu6y0fQJJO0h6cj8fpikTZtRsbotwrySyrE5AN5Kegi6q+159cqEEEIIdXRqVS5J44BRpB7Iy4BVSOuP7t7dFWv0jHAIac3NXUgf4KPAzZJOsn17d1ckhBBC/2X7qU4W+TdgB+C+XP5ZSU3pNm30jPA+4ALSA9ClwC2Sticto/NUnb7kEEIIoTu8nudzG0DSO5p1o0aB8APV3aC2HwB2k/S5ZlUohBBCIM0T/wkwJMeco4CfNuNGjZ4R1n0WaLsplQkhhBAAbJ8j6cPAS6TnhP9re2oz7tWj2ymFEEIIZUj6MnB9s4JfUVMWMA0hhBCW02BgiqTfSTohz2NvigiEIYQQ+hzbp9neGjiBtITbXZJubca9IhCGEELoy54D/kbahmm9DvJ2SQTCEEIIfY6k4yXdSdqtYhjwOdvbNuNeMVgmhBBCX/Qu4Et52l5TRSAMIYTQ59g+tafuFV2jIYQQWloEwhBCCC0tAmEIIYSWFoEwhBBCS+vzgVDSxpLukPSopNl5f0QkrSNpqqQ5+efahTJjJc3NG0DuV0jfKW8GOVfS+ZKU01eVdF1Ony5pRKHMmHyPOZLG9NwnDyGE0BP6fCAElgL/afu9wK7ACZK2Ak4FbrM9kjTP5FSAfO4wYGvSPooXSBqQr3UhcAwwMr8qG0UeDSy0vRlwLmkfRiStA4wj7cm4MzCuGHBDCKG/kTRE0iRJj+UGyPsbNTz6gz4fCG3Pt13ZmHEx8CiwEXAQMCFnmwAcnN8fBFxre4ntJ4G5wM6SNgAG255m28AVVWUq15oE7JNbi/sBU20vsL0QmEond1kOIYQVzHnAb2xvCWxH+p1bs+HRX/T5QFiUuyx3AKYD69ueDylY0rb0zkbA04Vi83LaRvl9dXq7MnkT4kXA0AbXqq7XMZJmSpq5lNe7/gFDCKEXSRoMfAC4BMD267ZfpH7Do19YYQKhpDWAG0grDbzUKGuNNDdI72qZtgR7vO1RtkcNZJUGVQshhD7t3cA/gMsk3S/p4rwzfL2GR7+wQqwsI2llUhC82vbPc/LfJW1ge37u9nwup88DNi4UHw48m9OH10gvlpknaSCwFrAgp+9ZVebObvpYIYSw3F55aRHTbrmpbPZhkmYWjsfbHl84HgjsCHzR9nRJ59HPukFr6fMtwvys7hLgUdvfL5yaDFRGcY4BbiykH5ZHgm5KGhQzI/8Vs1jSrvman6kqU7nWIcDt+TniFGBfSWvnh8P75rQQQlgRPV/pvcqv8VXn5wHzbE/Px5NIgfHvucFBVcOjX1gRWoS7A58GZkmqLL76X8BZwERJRwN/BQ4FsD1b0kTgEdKI0xNsv5nLHQ9cDqwO3JxfkALtlZLmklqCh+VrLZB0BnBPzne67QXN+qAhhNCbbP9N0tOStrD9OLAP6XfpI6TGwlm0b3j0C0oNn9BdBmmIN2OPt48fcukuixBCi5F0r+1Ry3ON6t85jcziVx3eT9L2wMXAKsATwJGk3sOJwCbkhkd/ahSsCC3CEEIIPSRve1QrWO7T03XpKX3+GWEIIYTQTBEIQwghtLQIhCGEEFpaBMIQQggtLQJhCCGElhaBMIQQQkuLQBhCCKGlRSAMIYTQ0iIQhhBCaGkRCEMIIbS0CIQhhBBaWgTCEEIILS0CYQghhJYWgTCEEEJLi0AYQgihpUUgDCGE0NIiEIYQQmhpEQhDCCG0tAiEIYQQWloEwhIkjZb0uKS5kk7t7fqEEEKztOLvuwiEHZA0APgx8BFgK+BwSVv1bq1CCKH7tervuwiEHdsZmGv7CduvA9cCB/VynUIIoRla8vfdwN6uwApgI+DpwvE8YJdiBknHAMfkw5dn8avHC+eaXsE+bBjwfG9Xoo+I76JNfBdttljeC/yTRVNm8athJbOvJmlm4Xi87fGF4w5/3/VHEQg7ViuSud1B+g9pfI18LU3STNujersefUF8F23iu2hTFZS6xPbo7qhL1uHvu/4oukY7Ng/YuHA8HHi2l+oSQgjN1JK/7yIQduweYKSkTSWtAhwGTO7lOoUQQjO05O+76BrtgO2lkr4ATAEGAJfant3L1VpRRHdxm/gu2sR30aZPfRet+vtOdr/v/g0hhBDqiq7REEIILS0CYQghhJYWgTB0maRBkn4l6TFJsyWdVTi3qqTr8jJN0yWNKJwbI2lOfo3pjbo3k6TJkh4uHLfcdyFpFUnjJf0p//fxiZzeit/F4ZJmSXpI0m8kDcvpLfdd9Fm24xWvLr2AQcBe+f0qwO+Aj+TjzwMX5feHAdfl9+sAT+Sfa+f3a/f2Z+nG7+TjwM+AhwtpLfddAKcB38zvVwKGteJ3QRqQ+Fzh838H+EYrfhd9+RUtwtBltl+1fUd+/zpwH2neEaRlmSbk95OAfZSW2dkPmGp7ge2FwFSgOycE9xpJawAnA9+sOtVy3wVwFPBtANtv2a6sJNNq34Xy6x35cw6mbV5eq30XfVYEwtAtJA0BDgBuy0lvL9VkeymwCBhK7SWcNuq5mjbVGcD3gFer0lvqu8j/LQCcIek+SddLWj+ntdR3YfsN4HhgFikAbgVckk+31HfRl0UgDMtN0kDgGuB8209UkmtkdYP0FZqk7YHNbP+i1ukaaf32uyB1Bw4H/mB7R2AacE4+11LfhaSVSYFwB2BD4CFgbOV0jSL99rvoyyIQhu4wHphj+weFtLeXasqBci1gAf13Caf3AztJ+gvwe2BzSXfmc632XbxAahVX/ii4Htgxv2+172J7ANt/dnoAOBHYLZ9rte+iz4pAGJaLpG+S/gf+UtWpyUBltNshwO35F8EUYF9Ja0taG9g3p63QbF9oe0PbI4A9gD/Z3jOfbrXvwsAvgT1z0j7AI/l9S30XwDPAVpLWzccfBh7N71vtu+i7enu0TrxW3BfpL1WT/sd+IL/+I59bjdQSmAvMAN5dKHdUTp8LHNnbn6MJ38sI2o8abbnvAngX8FtSV+BtwCYt/F0cl/8feYj0B8LQVv0u+uorllgLIYTQ0qJrNIQQQkuLQBhCCKGlRSAMIYTQ0iIQhhBCaGkRCEMIIbS0CIQhdJGkIZI+n99vKGlSb9cphNB5MX0ihC7K2+bcZHubXq5KCGE5DOztCoSwAjsLeI+kB4A5wHttbyPps8DBwABgG9JC3KsAnwaWAB+1vUDSe4AfA+uSliT7nO3Hev5jhNDaoms0hK47Ffiz7e2Br1ad2wb4d2Bn4EzgVds7kBag/kzOMx74ou2dgK8AF/RIrUMI7USLMITmuMP2YmCxpEWkpbUgbcezbd67cDfg+rQFHQCr9nw1QwgRCENojiWF928Vjt8i/X+3EvBibk2GEHpRdI2G0HWLgTW7UtD2S8CTkg4FULJdd1YuhFBOBMIQusj2C8AfJD0MfLcLl/h/wNGSHgRmAwd1Z/1CCOXE9IkQQggtLVqEIYQQWloEwhBCCC0tAmEIIYSWFoEwhBBCS4tAGEIIoaVFIAwhhNDSIhCGEEJoaf8f0B9aER4WpokAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with xr.open_dataset(\"vars_KIS_10km_0-00_0_1-0_air.nc\") as dataset:\n",
    "    variable = \"velbase_mag\"  # TODO change variable here\n",
    "    data_array = dataset[variable].sel({\"y\": 0})  # choose middle of the 3 'y' columns\n",
    "\n",
    "    # Plots filled contour, http://xarray.pydata.org/en/stable/generated/xarray.plot.contourf.html\n",
    "    ax = plt.axes()\n",
    "    xr.plot.contourf(\n",
    "        darray=data_array,  # NetCDF data array to plot\n",
    "        x=\"time\",  # time on x-axis\n",
    "        y=\"x\",  # x distance along flowline on y-axis\n",
    "        ax=ax,  # matplotlib ax to plot on\n",
    "        # figsize=(20, 10),  # figure size in (width, height)\n",
    "        levels=9,  # how many contour levels to use\n",
    "        cmap=plt.cm.PuOr_r,  # colormap to use, see https://matplotlib.org/tutorials/colors/colormaps.html#diverging\n",
    "    )\n",
    "    plt.savefig(fname=f\"contour_plot_of_{variable}.png\")  # saves figure to a file\n",
    "    plt.show()  # displays the plot, can disable by adding a # to the front"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method ImplementsDatasetReduce._reduce_method.<locals>.wrapped_func of <xarray.Dataset>\n",
       "Dimensions:                           (nv: 2, time: 191, x: 153, y: 3)\n",
       "Coordinates:\n",
       "  * time                              (time) object 0011-01-01 00:00:00 ... 0001-01-01 00:00:00\n",
       "  * x                                 (x) float64 0.0 1.007e+04 ... 1.53e+06\n",
       "  * y                                 (y) float64 -1.007e+04 0.0 1.007e+04\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    mapping                           float64 ...\n",
       "    pism_config                       int8 ...\n",
       "    time_bounds                       (time, nv) timedelta64[ns] ...\n",
       "    timestamp                         (time) timedelta64[ns] ...\n",
       "    beta                              (time, x, y) float32 ...\n",
       "    bfrict                            (time, x, y) float32 ...\n",
       "    bmelt                             (time, x, y) float32 ...\n",
       "    bwat                              (time, x, y) float32 ...\n",
       "    bwp                               (time, x, y) float32 ...\n",
       "    bwprel                            (time, x, y) float32 ...\n",
       "    climatic_mass_balance_cumulative  (time, x, y) float32 ...\n",
       "    dHdt                              (time, x, y) float32 ...\n",
       "    sigma_xx                          (time, x, y) float32 ...\n",
       "    sigma_yy                          (time, x, y) float32 ...\n",
       "    sigma_xy                          (time, x, y) float32 ...\n",
       "    effbwp                            (time, x, y) float32 ...\n",
       "    enthalpybase                      (time, x, y) float32 ...\n",
       "    enthalpysurf                      (time, x, y) float32 ...\n",
       "    flux_mag                          (time, x, y) float32 ...\n",
       "    hydrobmelt                        (time, x, y) float32 ...\n",
       "    hydroinput                        (time, x, y) float32 ...\n",
       "    mask                              (time, x, y) int8 ...\n",
       "    taub_x                            (time, x, y) float32 ...\n",
       "    taub_y                            (time, x, y) float32 ...\n",
       "    taub_mag                          (time, x, y) float32 ...\n",
       "    tauc                              (time, x, y) float32 ...\n",
       "    taud_x                            (time, x, y) float32 ...\n",
       "    taud_y                            (time, x, y) float32 ...\n",
       "    taud_mag                          (time, x, y) float32 ...\n",
       "    tempbase                          (time, x, y) float32 ...\n",
       "    temppabase                        (time, x, y) float32 ...\n",
       "    thk                               (time, x, y) float32 ...\n",
       "    topg                              (time, x, y) float32 ...\n",
       "    usurf                             (time, x, y) float32 ...\n",
       "    uvelbase                          (time, x, y) float32 ...\n",
       "    vvelbase                          (time, x, y) float32 ...\n",
       "    velbase_mag                       (time, x, y) float32 ...\n",
       "    uvelsurf                          (time, x, y) float32 ...\n",
       "    vvelsurf                          (time, x, y) float32 ...\n",
       "    velsurf_mag                       (time, x, y) float32 ...\n",
       "    wallmelt                          (time, x, y) float32 ...\n",
       "    tillphi                           (x, y) float32 ...\n",
       "    ice_surface_temp                  (time, x, y) float32 ...\n",
       "    climatic_mass_balance             (time, x, y) float32 ...\n",
       "    saccum                            (time, x, y) float32 ...\n",
       "    smelt                             (time, x, y) float32 ...\n",
       "    srunoff                           (time, x, y) float32 ...\n",
       "    air_temp                          (time, x, y) float32 ...\n",
       "    shelfbtemp                        (time, x, y) float32 ...\n",
       "    shelfbmassflux                    (time, x, y) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.5\n",
       "    command:       /usr/local/src/pism0.7/build/pismr -i ./end_KIS_10km_sia3-...\n",
       "    history:       /usr/local/src/pism0.7/build/pismr -i ./end_KIS_10km_sia3-...\n",
       "    source:       PISM stable v0.7.3-2-gf64551e>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.var"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	name: kambicestream
	channels:
	- conda-forge
	dependencies:
	- ipykernel=5.1.2
	- nc-time-axis=1.2.0
	- netcdf4=1.5.1.2
	- xarray=0.12.3