Skip to content

Instantly share code, notes, and snippets.

@niallrobinson

niallrobinson/coord_prob_nb

Created September 23, 2013 13:13
Show Gist options
  • Save niallrobinson/6670225 to your computer and use it in GitHub Desktop.
Save niallrobinson/6670225 to your computer and use it in GitHub Desktop.
Download ZIP
new plot coord problem
Raw
coord_prob_nb
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the correct version of Iris"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print iris.__version__"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1.5.0\n"
]
}
],
"prompt_number": 113
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plots fine when specifying using an actual coord object"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"qplt.plot(torn_count.coord('month_no'), torn_count)\n",
"plt.figure(num=None)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 116,
"text": [
"<matplotlib.figure.Figure at 0x6bde8d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEVCAYAAAAhANiZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XlcVPX6wPEP4FbhigrE4JJowjgquGsabhmTlrmBC5ZW\nP/PmdW29LWqm3bRNM1uuWibulmZBpKZ0NZcMV0zTFJQhU6RMFFSW7++Pg3PFJWQc5szyvF8vXw5n\nzpx5jsU8892er5dSSiGEEELYwFvvAIQQQrguSSJCCCFsJklECCGEzSSJCCGEsJkkESGEEDaTJCKE\nEMJmkkSEsKO0tDS8vb0pLCzUOxQhHEKSiHBZvr6+VK5cmcqVK+Pt7c3tt99u/XnJkiV6h+cSJOmJ\nW1VO7wCEsNW5c+esj+vXr8+8efPo0qVLqa6Rn59PuXLyayBrjoWtpCUi3M6FCxd44oknqFGjBn5+\nfowYMYKLFy8CkJSUhMFgYPr06QQFBTF8+HAmT57MgAEDeOSRR6hatSohISFs3brVer3XXnuN+vXr\n4+vrS0hISLFWTkFBAf/4xz+oUqUKISEhxMfHF4slLS2N7t27U7lyZQwGAzNnzrxh3Lm5uYwcOZLa\ntWtTvXp1HnnkEXJzcwEIDQ0tdu38/Hxq1arF7t27AdiwYQPh4eFUqVKFxo0bk5iYaD03MjKSV155\nhY4dO+Lr60unTp3IzMwEoFOnTgBUq1aNypUrs337dg4cOED79u3x9fXFz8+P/v372/TfQXgIJYQb\nqFevnvruu++UUkpNmDBBderUSZ05c0adOXNGRUZGqgkTJiillNq4caMqV66cmjhxoiooKFAXLlxQ\nEydOVJUqVVLr169XSin1wgsvqIiICOu1V69erU6fPm19XLFiRWWxWJRSSr399tvKZDKpU6dOqbNn\nz6r77rtPeXt7q4KCAqWUUi1atFDjx49X+fn56uDBgyogIEB99dVX172HJ554QvXp00edPXtW5eTk\nqN69e6sxY8YopZR69dVX1eDBg63nfv311yosLEwppdSvv/6qqlWrZo0/KSlJVa1aVf32229KKaXu\nvfdeFRISoo4dO6Zyc3NVZGSkGj9+vFJKqbS0NOXl5WWNVyml+vTpo6ZNm6aUUiovL09t377dtv8o\nwiNIEhFu4cokEhQUZP1AVUqpDRs2qICAAKWUlkRuu+02lZeXZ31+4sSJqnv37taf9+/fr8qVK3fD\n92rZsqVavny5Ukqpdu3aqfnz51ufS0pKsn4oHzp0SFWoUEHl5ORYn3/llVdUTEzMNde8ePGiqlSp\nkjpy5Ij12JYtW1RgYKBSSqnDhw+rypUrq9zcXKWUUoMGDVJTpkxRSik1adIkFRsbW+x6PXr0UB99\n9JFSSqnIyEg1depU63Nz5sxRXbt2VUoplZqaek0SGTp0qBoxYoTKyMi44b+BEJdJd5ZwOydPnqRO\nnTrWn4ODgzl16pT1Zz8/v2vGQfz9/a2Pb7/9dgoKCqyDzR9//DFNmjShatWqVK9end27d1vHYzIz\nMzEYDNbXBgUFFYvDz8+P2267rVgsJ0+evCbmzMxMLl68SIsWLahevTrVq1cnKiqKs2fPAhASEkJo\naChr1qwhJyeHr776ikGDBgFgsVhYsWKF9XXVq1fnhx9+4I8//rBePyAgwPr4tttus3bvXc+///1v\nLl26RKtWrQgNDeXjjz++4blCyIiicDv+/v4cO3aMhg0bApCenk7t2rVveL6Xl9cNnzt8+DBjx47l\n+++/p1WrVgC0atXKOhBdu3ZtLBaL9fwrH/v7+5OVlUVubq41kaSnpxf7QL/Mz8+P8uXLc/jwYWrW\nrHndWAYOHMiSJUsoKCggLCyMu+66C4DAwECGDx/O+++/f8P7uJHr3XtgYCDz588HYOvWrXTu3JnI\nyEgaNWpU6usL9yctEeF2oqOjee211zhz5gx//fUXU6ZMsX5rvx71NzOTcnJyUEpRtWpVlFIsXryY\nPXv2WJ/v168f7777LpmZmWRnZ/PGG29Yn2vYsCFNmjTh5ZdfpqCggF9++YX//Oc/DBw48Jr3qVSp\nErGxsUyYMIEzZ84A8Pvvv/Pdd99Zz4mJieHbb7/lww8/ZPDgwdbjsbGxrFq1io0bN6KUIi8vjx9+\n+IHffvutxHusVq0aXl5epKamWo+tXr2a33//HYAqVarg7e39t4lWeDZJIsLtTJ06lZCQEO666y7q\n169PgwYNmDZtmvX5qz8Qvby8rnsMoFmzZjz11FO0bNmSgIAAkpOT6dChg/W80aNH0759exo0aEBE\nRAS9evUqdq2VK1eye/duqlWrRpcuXXj22Wfp1avXdeOePXs21atXJzQ0lCpVqnDvvfeSkpJifT4g\nIID27duzdetWoqOjrccbNmzIkiVL+Ne//kXVqlUJCAjgtddeK7b248qYrrzfqlWrMn78eFq2bEmN\nGjXYvn07mzdvJjw8nDvuuAOz2cz06dOtrTohrual/u5rmBBCCPE37N4SGT58OP7+/phMpmuee+ut\nt/D29i424Dd69GiMRiMRERHs2rXLenzBggUYjUaMRiOfffaZ9XhycjLh4eEYjUbGjBlj7/CFEEKU\ngt2TyLBhw4otdLosPT2ddevWUbduXeuxzz//nOPHj7N//37mzZvHsGHDADhx4gRTpkxh+/btbN++\nnVdffdU6u2bYsGHMnz+f/fv3c+zYMVatWmXvWxBCCHGT7J5EOnbsSPXq1a85Pn78eKZPn17sWEJC\nArGxsQCEh4eTn5+PxWJh3bp1REVF4evri6+vL/fffz9r167l+PHjFBYWEh4eDsCQIUOuWSEshBDC\ncRwyxffLL7/EYDDQtGnTYsctFgvBwcHWnw0GAxaLhYyMjGJz7688fuX5QUFBxaZUXiYzSYQQwjal\nHSYv89lZOTk5TJs2jcmTJ1uPXRlkWY3rK201vlv+mThxou4xyP3Jvcn9ud8fW5R5Ejly5AhpaWk0\na9aM+vXrY7FYaNGiBSdPnsRgMJCenm4993LL5Orj6enp1z1usViKtViEEEI4VpknEZPJxMmTJ0lN\nTSU1NRWDwcDOnTvx9/fHbDazaNEiAHbu3ImPjw9BQUF07dqVxMREsrOzyc7OJjExkW7duhEcHIy3\nt7d1FteiRYswm81lfQtCCCFuwO5JZODAgbRv355Dhw4RHBzMJ598Uuz5K8cr+vbtS1BQEEajkccf\nf9x67p133smLL75ImzZtaNOmDa+88oq1ttEnn3zC8OHDMRqN1KlThz59+tj7FpxeZGSk3iGUKXe+\nP3e+N5D780RuudjQy8vL5v49IYTwVLZ8dkrZEyGEEDaTJCKEEMJmkkSEEELYTJKIEEIIm0kSEUII\nYTNJIkIIIWwmSUQIIYTNJIkIIYSwmSQRIYQQNpMkIoQQwmaSRIQQQthMkogQQgibSRIRQghhM0ki\nQgghbCZJRAghhM0kiQghhLCZJBEhhBA2kyQihBDCZpJEhBBC2EySiBBCCJtJEhFCCGEzSSLC4508\nqXcEQrguSSLCo+3eDYGB8OqroJTe0QjheuyeRIYPH46/vz8mk8l6bPz48YSFhREWFkbPnj3Jysqy\nPvf6668TFhaGyWRi7dq11uOJiYmYTCbCwsJ44403rMdTU1Np164dJpOJmJgY8vLy7H0LwoPMmgVj\nxkBCAkRHQ06O3hEJ4WKUnf33v/9VO3fuVE2aNLEe27BhgyooKFBKKfXcc8+psWPHKqWU+umnn1TL\nli1Vfn6+slgsql69eurSpUvqwoULql69espisai8vDzVsmVLtXPnTqWUUj179lSrVq1SSik1ZswY\n9fbbb18TQxnclnBDp04pVa2aUpmZSuXmKhUbq1R4uFLHj+sdmRD6sOWzs5y9k1LHjh1JS0srdqxz\n587Wxx06dGDhwoUAxMfHExMTg4+PD0FBQRiNRrZv305hYSFGo5GgoCAAoqOjiY+Px2QysW3bNr76\n6isAhgwZwvPPP8+4ceOuiWPSpEnWx5GRkURGRtr3RoXL+89/oG9fqFlT+3nBApgxA9q2hc8/1/4W\nwp0lJSWRlJR0S9ewexIpyccff0xMTAwAGRkZdOnSxfqcwWDAYrGglCI4OLjY8aSkJDIzM6l5+Tce\nCAoKwmKxXPd9rkwiQlwtLw/mzNG6sS7z8oJnn4WwMOjVC956C4YO1S9GIcra1V+wJ0+eXOprODSJ\nTJ06lQoVKjB48GBHvq0Q1/jiC2jYEJo2vfa5nj0hKQkefBBSUuD118HHx+EhCuESHDY7a8GCBcTH\nx7No0SLrMYPBQHp6uvVni8VCcHDwNcfT09MJDg6mdu3anD59utj5BoPBMTcg3MqsWTB69I2fNxph\n+3bYsQMeegjOnnVcbEK4EockkcTERKZPn86aNWuoVKmS9bjZbGbZsmXk5+djsVhISUmhdevWtGrV\nipSUFDIyMsjLy2P58uVERUXh4+ND27ZtWb16NQBxcXGYzWZH3IJwIz/9BBkZWpfV36lZE9auheBg\naNcOjhxxTHxCuBR7j+7HxMSowMBAVb58eWUwGNS8efNUSEiIqlOnjmrevLlq3ry5GjlypPX8qVOn\nqtDQUGU0GlViYqL1eEJCgjIajSo0NFRNmzbNevzo0aOqbdu2qkmTJio6OlpdunTpmhjK4LaEG4mN\nVWr69NK95v33lfL3V2rDhrKJSQhnYMtnp1fRC92Kl5cXbnhbwg5+/x1CQ+HoUahevXSv3bABBg2C\niRNh5MiyiU8IPdny2Skr1oVH+egjbVFhaRMIQJcusHkzvPcePPWUNsNLCE8nLRHhMS5dgrp1Yf16\nbeDcVn/9pbVIcnNhxQrw87NfjELoSVoiQvyNFSugSZNbSyAAVavCmjXQogW0aQM//2yf+IRwRZJE\nhEdQCmbO/PtpvaXh46Otbn/5ZYiMLL5oUQhPIklEeITt2yErC+w9I/yRR+DLL+GJJ+DNN6USsPA8\nMiYiPMKgQdC6NYwdWzbXT0/XFiU2baoN3lesWDbvI0RZsuWzU5KIcHsZGWAyQWqqNp5RVs6fh0cf\n1d7viy8gIKDs3kuIsiAD60Jcx4cfai2RskwgAHfcAcuWQY8e2oD7rl1l+35COANpiQi3duGCNq33\nv/+Fu+923PuuXKktSPzgA+jXz3HvK8StsOWz0+Gl4IVwpGXLICLCsQkEtMTRoAH07g3792uzuLyl\n3S/ckLREhNtSSlvLMXUqREXpE8PJk/DwwxAUBJ9+qnV5CeGsZExEiCv88IM22N2jh34x+PvDxo3g\n6wsdO2qzuIRwJ5JEhNuaNQv++U/9u5EqVoT582HwYG3L3a1b9Y1HCHuS7izhltLToXlzSEuDypX1\njuZ/4uNh+HCwWKB8eb2jEaI46c4SosicORAb61wJBOCBByAkRCsCKYQ7kJaIcDu5uVCnjtZtFBKi\ndzTXmj1bK8OycKHekQhRnLREhAAWLdLGHpwxgQD07w9ffQU5OXpHIsStkyQi3IpS2oC6var1lgV/\nf21F+9df6x2JELdOkohwK99/D/n50K2b3pH8vUGDYPFivaMQ4tbJmIhwKw8/rK0LefJJvSP5e2fP\nQnCwNnvMlq16hSgLMiYiPFpqKmzapM3KcnZVqkD37lq1XyFcmSQR4TbmzIFhw1yntMigQbBkid5R\nCHFr7J5Ehg8fjr+/PyaTyXrsjz/+oHv37jRt2pQePXpw5swZ63OjR4/GaDQSERHBritqZy9YsACj\n0YjRaOSzzz6zHk9OTiY8PByj0ciYMWPsHb5wUefPwyefwFNP6R3JzTObYedOOHFC70iEsJ3dk8iw\nYcNITEwsdmzixIk88MAD7N27l6ioKCZOnAjA559/zvHjx9m/fz/z5s1j2LBhAJw4cYIpU6awfft2\ntm/fzquvvsqpU6es158/fz779+/n2LFjrFq1yt63IFzQwoVabap69fSO5OZVqqTthrhsmd6RCGE7\nuyeRjh07Uv2qkcKEhARiizqqhwwZQnx8PADx8fHW4+Hh4eTn52OxWFi3bh1RUVH4+vri6+vL/fff\nz9q1azl+/DiFhYWEh4dfcy3huVxhWu+NDBwoXVrCtTlkP5HMzEz8/PwAqFmzprVVkZGRQXBwsPU8\ng8GAxWIhIyMDg8Fw3eNXnh8UFITFYrnue06aNMn6ODIyksjISDvekXAm69dDuXLgiv+Ju3SBoUPh\n11+dd3GkcF9JSUkkJSXd0jV035SqrKbiXplEhHu73Arx8tI7ktIrV05bwb50Kbz0kt7RCE9z9Rfs\nyZMnl/oaDpmdVatWLU6fPg1orZLatWsDWgsj/YoNFiwWC8HBwdccT09Pv+5xi8VSrMUiPM+vv8K2\nbdpMJ1d1eeGhLG0SrsghScRsNhMXFwdAXFwcZrPZenzRokUA7Ny5Ex8fH4KCgujatSuJiYlkZ2eT\nnZ1NYmIi3bp1Izg4GG9vb+ssrkWLFlmvJTzT7Nnw+ONw++16R2K7tm21opF79+odiRA2UHYWExOj\nAgMDVfny5ZXBYFDz589XWVlZqlu3bspkMqnu3burP//803r+U089pcLCwlR4eLhKTk62Hp8/f74K\nDQ1VoaGh6tNPP7Ue/+mnn1Tz5s1VWFiY+uc//3ndGMrgtoQTOntWqerVlTp2TO9Ibt0LLyj17LN6\nRyE8nS2fnVL2RLis2bO1WlkrVugdya3bt0/bayQtTf+dGIXnkrInwmMUFsJ774G7rDc1maBqVdiy\nRe9IhCgdSSLCJX37rVbepEMHvSOxH6nsK1yRdGcJlxQVBdHR8OijekdiP6mp2j4jGRmy/7rQh3Rn\nCY/wyy9azamYGL0jsa/69WX/deF6JIkIl/Pee/B//6fVnnI3UgZFuBrpzhIu5a+/tG/sKSlw5516\nR2N/J09C48Zal5Yrr30RrqlMurOys7MpKCgA4JdffuGLL77g4sWLtkUoxC365BO4/373TCCg7b/e\nqpXsvy5cR4lJpFOnTuTn55Oens59993HkiVLeNSdRjOFyygo0LqyXLFab2nIZlXCldzUmEjFihVZ\ntWoVo0aNYsWKFfz8889lHZcQ10hIAD8/bQaTO3v4YdiwAa7Yu00Ip3VTSWTHjh0sWbLEWqdKxhuE\nHly5Wm9pVK0K3brJ/uvCNZSYRN58800mTZpE7969MRqNpKWl0alTJ0fEJoTV/v3aYPqAAXpH4hiy\n8FC4ihL3E8nKyiq2e2C9evUkiQiHe+89ePJJqFBB70gcw2zWqhOfOAGBgXpHI8SNlTjFNzw83Fp6\n/bJmzZqxZ8+eMg3sVsgUX/fy559w111w4AAEBOgdjeM8+iiEh7tPfTDh/Gz57LxhS+Sbb74hISGB\njIwMRo8ebb1wTk4OXu7eKS2cyty50KuXZyUQ0BYevvKKJBHh3G44JnLnnXfSokULKlWqRIsWLax/\nevTowbp16xwZo/Bg+fnw/vvwz3/qHYnjde2qlYY/ckTvSIS4sRK7s/Ly8ijvYtXgpDvLfaxaBTNm\neG6J9FGjtBaY7L8uHKFMVqxv2rSJyMhIGjRoQP369alfvz533XWXzUEKURozZ3p2d47svy6cXYkt\nkfr16zNnzhwiIiLw8fGxHq9Zs2aZB2craYm4hz17tN3+UlM9tzS6UlqtsC+/hGbN9I5GuDu7Dqxf\nFhgYSFRUlM1BCWGr996DkSM9N4GAtrDycmVfSSLCGZXYEnn++ecBeOihh6hYsaL1eERERNlGdguk\nJeL6Tp+Ghg3h0CGoVUvvaPS1bx/07Km1yGT/dVGWyqQlsm3bNry8vNi+fXux4xs3bixddEKUwn/+\no9WQ8vQEAtr+65Ura5ML7rlH72iEKE72ExFOJy9PW1y4Zo222E7AtGnaHiPvv693JMKdlcnsrIyM\nDIYMGUL37t0BbU+Rjz/+2LYIhbgJq1drg8mSQP4nJgZWrNASrBDOpMQkMmTIEHr16sXJkycBaNCg\nAbNmzbLpzSZOnEijRo1o3Lgx/fr1Iycnh9TUVNq1a4fJZCImJoa8ot+SixcvEh0djclkokOHDhw7\ndsx6nddff52wsDBMJhNr1661KRbhvDx9Wu/13HUXNGgA332ndyRCFFdiEsnKyiI6Oto6vbdcuXKU\nK1fiUMo1fv31VxYuXEhKSgoHDx7Ex8eHJUuWMHr0aJ577jn27dtHQEAAs2fPBmD27NkEBgayb98+\nnnnmGUYX7USUnJzMF198wb59+0hMTGTEiBFcunSp1PEI57R3r7ZK+6GH9I7E+QwcKJV9hfMpMYnc\ncccdZGVlWX/etWtXsVlaN6tGjRqUL1+e8+fPk5+fT05ODnXq1GHbtm307t0b0Fo9lysGJyQkEBsb\nC8CDDz7Ili1bKCwsJD4+npiYGHx8fAgKCsJoNPLjjz+WOh7hnD76SKtea8P3FLc3YAB89RXk5uod\niRD/U+Kv6ttvv819993H0aNH6dSpE8ePH2fFihWlfqMaNWowYcIE6tSpw2233UaPHj1o0qRJsUWL\nQUFBWCwWACwWC8HBwQB4e3vj5+fHqVOnyMjIoEuXLtbXGAwG62uuNGnSJOvjyMhIIiMjSx2zcKxz\n57T1EHv36h2JcwoIgJYttf3X+/fXOxrhDpKSkkhKSrqla5SYRNq1a8e2bdvYs2cPSimaNm1qU0vk\nyJEjvPvuu6SlpVG1alX69+9fpoUcr0wiwjUsXQodO4LBoHckzutyGRRJIsIerv6CPXny5FJfo8Tu\nrKZNmzJjxgyqV69Oq1atbEogAD/++CPt27fHz8+PcuXK0adPH/773/9y+vRp6zkWiwVD0SeIwWDg\n+PHjABQWFpKVlUWtWrUwGAykp6cXe83lFotwbR99BCNG6B2Fc+vTR/ZfF86lxCSyZs0afHx8GDBg\nAC1btuTNN9+0friXRkhICNu2bSM3NxelFOvXr6dx48a0bduW1atXAxAXF2fdx91sNhMXFwfAl19+\nSbt27fDx8cFsNrNs2TLy8/OxWCykpKTQunXrUscjnMvOnZCZCT166B2Jc6taVSsRL/uvC2dRqsWG\nhw8fZsqUKSxatIiCgoJSv9mkSZNYtGgR3t7ehIeH8+mnn3LixAkGDRrEuXPnMBqNLFy4kPLly3Px\n4kViY2M5cOAAlStXZvHixdSrVw+AadOmERcXh7e3N2+99RY9rvrkkcWGrmfECKhTB158Ue9InN/K\nlVqrTbb1EfZmy2fnTSWRtLQ0li1bxvLly/Hx8SE6OpoJEybYHGhZkyTiWs6ehbp14eefZT/xm5Gb\nC3fe6XnbBYuyVya1s9q0acOlS5cYMGAAK1askL1EhN0tXgxdukgCuVm33QYPPgjLl0PR8ikhdFNi\nS+TgwYM0btzYUfHYhbREXIdSWnmTGTOgqLKOuAmJiTBpEmzbpnckwp2USe2sypUrS+0sUWZ+/BGy\ns7XBYnHzunaFo0dl/3WhP4fWzhLiah99BP/3f7JPRmmVL6+tFVm6VO9IhKdzWO0sIa525gysWgXD\nhukdiWuS/deFM3BY7SwhrrZwobYupHZtvSNxTe3awfnz2s6HQujlpmpn9ejR45ZrZwlxJaW0rqz3\n3tM7Etfl7a3tM7J4MTRtqnc0wlPd1DqRS5cusbeoKp7JZHL6lojMznJ+P/wAw4fDwYPg5aV3NK5r\n715tuu/RozKuJG5dmawTAahQoQItW7a0KSghrufDD7VV6pJAbo3JBHfcAVu3QocOekcjPJHssS4c\nLitL26XvyBHw89M7Gtc3dSr89pvsvy5uXZmsExHC3j77DHr2lARiL7L/utDTTXVnLVu2jE2bNgFw\n77330l82MxA2ujygPneu3pG4jwYNtD3Yv/sO7r9f72iEpymxJTJ27Fjmzp1LREQE4eHhzJ07l7Fj\nxzoiNuGGvv8efHyk/97eBg7UdoUUwtFKHBMJCwsjJSUF76KpH4WFhRiNRg4cOOCQAG0hYyLOa+BA\nbX2DFA60rxMnICxMGxu57Ta9oxGuqszGRM6ePXvdx0KUxqlTWuHA2Fi9I3E/gYHa/uvx8XpHIjxN\niWMizzzzDE2aNKFbt24opdiwYQOvvvqqI2ITbubTT6F3b6heXe9I3NPAgdrCw3799I5EeJKbmuJ7\n/Phxtm3bhpeXF+3atbPug+6spDvL+RQWQqNGEBcHbdvqHY17OnNG29zr2DGoVk3vaIQrsuvOhsnJ\nyXhdsRLs8mmXj0VERNgaZ5mTJOJ81q+Hp5+GXbtkgWFZevhhbQW7FLUUtrBrEomMjMTLy4vc3FyS\nk5NpWlScZ+/evbRs2ZKtW7feesRlRJKI8+nXT9sDY+RIvSNxbytWwMcfy/7rwjZ2HVhPSkpi48aN\nGAwG9uzZQ3JyMsnJyezdu9fpu7OEc/n9d20Nw+DBekfi/nr2hB07tH9zIRyhxNlZBw4cICwszPpz\naGgoP//8c5kGJdzL/PlaS6RKFb0jcX9X7r8uhCOUmERCQkIYMWKEtWXy5JNPEhIS4ojYhBsoKNC6\nV558Uu9IPIcsPBSOVOLsrJycHGbOnMnmzZvx8vLinnvuYcyYMdzmxCuaZEzEeXzzDbz8Mvz0k96R\neI68PAgKgm3btHIoQtwsuw6sl4UzZ87wxBNPcOjQIS5dusQnn3xCo0aNiI6O5uTJkwQGBrJs2TKq\nFc1PHD16NN999x0VK1Zk3rx5hIeHA7BgwQKmT58OwHPPPcfQoUOL35QkEafRuzc88AA88YTekXiW\nf/xDSyQvvqh3JMKV2PTZqUqwf/9+1bNnT9WwYUNVr149Va9ePVW/fv2SXnZd/fr1U4sXL1ZKKVVQ\nUKD++usvNWrUKPXOO+8opZR655131OjRo5VSSq1cuVI99NBDSimldu7cqZo1a6aUUuq3335TDRo0\nUNnZ2So7O1s1aNBA/f7778Xe5yZuSzhAerpS1asrlZ2tdySeZ9MmpcLClCos1DsS4Ups+ewscUwk\nNjaWMWPGUKlSJZKSkhg+fDiDbZhmk5WVxe7duxk4cCAA3t7eVKlShYSEBGKL6mAMGTKE+KK6DfHx\n8dbj4eHh5OfnY7FYWLduHVFRUfj6+uLr68v999/POpnP6JTmzdPKlPv66h2J52nfHs6dk/3XRdkr\nsexJfn7+PKtPAAAawElEQVQ+3bp1o7CwkLp16/Lyyy/TqlUrpkyZUqo3Onz4MLVq1WLAgAH8/PPP\nRERE8P7775OZmYlf0cYSNWvW5NSpUwBkZGQQHBxsfb3BYMBisZCRkVFsivHl41ebNGmS9XFkZCSR\nkZGlilfcmvx8rdz711/rHYlnurz/+pIlsv+6uLGkpCSSkpJu6RolJpHbb78dpRR169Zlzpw5BAQE\nkJWVVeo3KiwsZMeOHcycOZNWrVoxduzYEhORuoVxjSuTiHC8b74BgwGaNdM7Es81aBA89BBMmyZV\nAsT1Xf0Fe/LkyaW+RondWTNnzuT8+fPMnj2bzZs3ExcXR1xcXKnfKDg4mKCgIFq1agVAv3792L17\nN7Vr1+b06dMAZGZmUrt2bUBrYaSnp1tfb7FYCA4OvuZ4enp6sRaLcA6X91AX+mnaFG6/Xdt/XYiy\nUmISad26Nb6+vtSvX5/FixfzxRdf0L59+1K/UXBwMDVr1uTQoUMArF+/ntDQUKKioqxJKS4uDrPZ\nDIDZbGbRokUA7Ny5Ex8fH4KCgujatSuJiYlkZ2eTnZ1NYmIi3bp1K3U8ouwcO6ZNLx0wQO9IPJuX\nl9YaWbhQ70iEOyuxOyslJYU333yT9PR0CgsLAW0a2IYNG0r9ZvPmzWPw4MHk5ORQt25dFi1ahFKK\n6Oho5s+fT0BAAMuLltr27duXjRs3YjQaqVixIp988gkAd955Jy+++CJt2rQB4JVXXsHf37/UsYiy\nM3euVuLk9tv1jkQMG6btM/LAA1pJFCHsrcR1InfffTdjx44lIiICHx8f7UVeXrRo0cIhAdpC1ono\nJy9PK0e+bh0YjXpHI0BrFfbqpW0I5sS/tsIJ2PLZWWJLpGrVqoyU0qviJn31FTRoIAnEmbRtq41R\nPfigNj5Sp47eEQl3UuKYiNls5sMPP+TEiRP88ccf1j9CXM9HH8mAujPq2xcmTACzGf76S+9ohDsp\nsTurXr16xTanAq3Jc/To0TIN7FZId5Y+jh6FNm0gPR0qVdI7GnE1pWD0aDhwABISoEIFvSMSzsbu\ntbMKCwtZuXIlA1xsmo0kEX08/7w2JvLWW3pHIm6koEDb/bBmTa2igKwfEVcqkwKMbdq0Yfv27bcU\nmKNJEnG8S5e0vvbvv4e779Y7GvF3zp+He+/VimO+9JLe0QhnYtedDS/r3Lkz77zzDunp6TImIm5o\n9WoIC5ME4gruuEObADF3LtiwbliIYmRMRNhFly7agHp0tN6RiJu1fz907qzty37vvXpHI5yB0+8n\n4iiSRBzr0CHo2FEbUJfBWtfy3XfaqvakJAgN1TsaobcyWSdy8eJF3n33XTZt2oSXlxedOnVizJgx\nVJBPC1Hk44+1ldHyv4Tr6doV3nhDW9G+dStI8QdRWiW2RAYPHkzFihUZMmQISimWLFlCbm6uta6V\nM5KWiONcuADBwdqq6AYN9I5G2GriRK3yclKSlKvxZHbtzsrPz6dcuXIYjUb2799f7LnrHXMmkkQc\nZ9EiWLAA1q7VOxJxK5SCRx6B7GxYuRKKKhwJD2PX2VmtW7e2XjQtLc16PC0tDW/vEid1CQ8hK9Td\ng5eXNlvrr7/g6af1jka4khuOiVzORtOnT6dt27Y0btwYpRSHDh1i3rx5DgtQOK/9++HXX7WaTML1\nVagAn38OHTrArFna6nYhSnLD7iyDwcD48eNRSpGTk0OlojoWFy9e5Pbbb2f8+PEODbQ0pDvLMcaM\ngcqV4bXX9I5E2FNamrZH+wcfaDsjCs9h19lZBQUFZGdnW3/OycmxPr7yuPBMubnaeEhyst6RCHur\nVw++/FIr1njnnVC0GakQ13XDlkh4eDi7du1ydDx2IS2RsrdgASxfDvHxekciysqXX8LIkbBli5ZY\nhPsrk3UiQlzPhx/CCy/oHYUoSw89pG11bDZriaRaNb0jEs7ohi2RrKws/Pz8HB2PXUhLpGzt3ast\nTktNhXLyNcTtjR2r/TdPTJQFpe5Oyp4UkSRStp56CmrX1haoCfdXUKBtalWlitaNKeXj3ZckkSKS\nRMrOuXNayfe9e8Fg0Dsa4Sjnz2vFGs1mmDRJ72hEWZExEVHmli7Vii1KAvEsl8vHt2sH9etrq9uF\nAEkiopQ++ggmT9Y7CqEHf39tNl5kpPYlomtXvSMSzkDql4iblpwMmZnQo4fekQi9hIbCsmUwcKBW\nsUAIhyeRgoICwsPD6dWrFwCpqam0a9cOk8lETEwMeXl5gLYyPjo6GpPJRIcOHTh27Jj1Gq+//jph\nYWGYTCbWSuU/h/noI3jiCSnO5+kiI+Htt7UZer//rnc0Qm8OTyIzZ84kLCzMulvi6NGjee6559i3\nbx8BAQHMnj0bgNmzZxMYGMi+fft45plnGF1UyCc5OZkvvviCffv2kZiYyIgRI7h06ZKjb8PjnD2r\n7YA3fLjekQhnMGQIPPYY9OypDboLz+XQJGKxWEhISODxxx9HKUVBQQHbtm2jd+/eAAwZMoT4oiXQ\nCQkJxMbGAvDggw+yZcsWCgsLiY+PJyYmBh8fH4KCgjAajfz444+OvA2PtGiR1gceGKh3JMJZvPQS\nmExa11ZBgd7RCL04dGB93LhxzJgxg7NnzwJw6tQpatasaX0+KCgIi8UCaAknODgYAG9vb/z8/Dh1\n6hQZGRl06dLF+hqDwWB9zZUmXTEPMTIyksjIyDK4I8+glNaVNWOG3pEIZ+Llpf1/YTZrCxJnzZI1\nJK4mKSmJpKSkW7qGw5LI119/Te3atQkPD7cGXZZrOSbJZHa7+fFHbbMimY0jrlahgraJ1T33wMyZ\nWjIRruPqL9iTbZh66bAksmXLFtasWUNCQgIXLlzg7NmzPPfcc5w+fdp6jsViwVC0AMFgMHD8+HFq\n165NYWEhWVlZ1KpVC4PBQHp6erHXXG6xiLJxeeMp2YtMXE+1atrU3/bttYWoffroHZFwJId9LEyb\nNo309HRSU1NZunQpXbp0YeHChbRt25bVq1cDEBcXh9lsBsBsNhMXFwfAl19+Sbt27fDx8cFsNrNs\n2TLy8/OxWCykpKRYd2EU9vf995CQAMOG6R2JcGZ168KaNdqXjc2b9Y5GOJJu3y0vz86aNWsWb7zx\nBiaTiZMnT/LPf/4TgFGjRvHbb79hMpmYMWMGs2bNAqBFixY8/PDDNG3alPvvv5+PPvqI8uXL63Ub\nbu3ECRg0SKuXVKuW3tEIZ9eiBXz2mVZnq39/2LNH74iEI0jtLHFd+fnQrZu2JkCGl0RpnDundYG+\n+Sa0bg0vvwwtW+odlbgZtnx2Si+3uK6XXoKKFbUPACFKw9cXJkyAo0e1LyIPPwxRUdqeJML9SEtE\nXGPNGhg1SitzIt1Y4lZdvAiffgqvvw4NGmhfTO69V6YDOyMpBV9Ekojtjh6Ftm21rVHbtdM7GuFO\n8vIgLg6mTYOAAK21e999kkyciSSRIpJEbHPhgjZN89FHoajKjBB2l5+vFXGcOhUqV9aSSc+ekkyc\ngSSRIpJEbDNiBPz5p/YLLr/QoqwVFsIXX8CUKdoapJde0sZPZD2SfiSJFJEkUnqffaZ9M9yxQ9sG\nVQhHKSyEr7/WkkluLrz4IgwYINWi9SBJpIgkkdLZtw+6dIGNG6FJE72jEZ5KKfj2Wy2ZnD4N//qX\ntk5JloE5jiSRIpJEbt7Zs9CqldaVUFQ0WQhdKaV9oZkyBY4dgxde0LbjrVBB78jcnySRIpJEbo5S\nWreBnx98+KHe0Qhxrc2btWRy4AA895y2h0mlSnpH5b5ksaEolVmztCm9776rdyRCXN8992hdXCtX\nQmIi3HWXtquibITlPKQl4qG2bNFmwmzbBvXr6x2NEDdn1y547TWthTJuHDz1lDZNWNiHtETETcnM\nhOhomDtXEohwLeHh8PnnsGED7N2rtUwmTYJTp/SOzHNJEvEwBQXajJchQ6BXL72jEcI2RiMsXgw/\n/KBVm777bvi//4ODB/WOzPNIEvEwr76qrRieMkXvSIS4dY0aaRWDf/kF7rxTq8nVs6c2u0t6tB1D\nxkQ8SGKiNrslOVmrXSSEu8nNhYULtcH3O+7Qqgn37y9rTW6WTPEtIknkWsePa3s7LF8OnTrpHY0Q\nZauwUNuR8803tRmIo0fDE09A1ap6R+bcZGBdXNelS9q3sQkTJIEIz+DtrXVrJSXBqlWwc6c2iWT8\neG0Bo7AfSSIeYMIECAyEp5/WOxIhHK9FC20QfvduLblERMDAgfDTT3pH5h4kibi5pUu1Zv2nn0pl\nXuHZ6tTRurdSU7VSP337agPxa9Zo3V/CNjIm4sYOHNC6r9au1ebXCyH+Jy9PW3Py1ltaDblx42Do\nULj9dr0j048MrBeRJALnzkGbNtovxuOP6x2NEM5LKdi0SWulbNsGTz6prYT399c7MseTgXUBaL8U\nTz6pzcZ67DG9oxHCuXl5aS32NWu0ZHLqFDRurM3mOnBA7+icn7RE3NCHH8KcOdq3Kk9umgthq8xM\n+OADeP99aNlSm5zSuXPZjCteuqT1HJw/r/25/PjcOcjJ0basDgqy//tej1N3Z6WnpzN48GD+/PNP\nLl26xGOPPcazzz7LH3/8QXR0NCdPniQwMJBly5ZRrVo1AEaPHs13331HxYoVmTdvHuFFHfsLFixg\n+vTpADz33HMMHTq0+E15cBL56Scwm7UCdY0a6R2NEK4tNxfi4rTFi5UqacmkfftrP+yvlwBudOzq\n5wB8fbXFkVf/XaGC1jp67DF4/nmoUaNs79epk8jJkyfJzMykSZMmnDt3joiICFasWMHcuXNp0KAB\nY8eO5d133yU1NZWZM2fy+eefs3DhQlavXs2uXbsYNmwYu3fv5sSJE3Ts2JHdu3cD0Lx5c3744Qf8\nr+jA9NQk8scf2nTGN9/UZp4IIeyjsBC++QbeeQeOHPnfh/z1PvhL+1xJm21lZGhlilau1Na5jBmj\nva4s2PTZqXTSt29fFR8fr+666y51+vRppZRSmZmZqkGDBkoppYYNG6ZWrlxpPd9oNKr09HS1YMEC\nNWrUKOvxp556Si1cuLDYtXW8Ld0UFChlNis1bpzekQghysKhQ0pFRysVGKjU++8rdfGi/d/Dls/O\ncmWRzUqSlpbGjh07mD9/PpmZmfj5+QFQs2ZNThXVdM7IyCA4ONj6GoPBgMViISMjA4PBcM3xq02a\nNMn6ODIyksjIyLK5GSfx73/DmTPwxht6RyKEKAsNG2rrvnbu1Paff+strYUSE6MtorRFUlISSUlJ\ntxSXw5PIuXPn6NevHzNnzqRKlSp/e666hS6pK5OIu9uwAd57TxsPkUJzQri3iAitmOrGjdr+89On\nw+uvw/33l37g/+ov2JMnTy51PA6d4puXl0ffvn0ZPHgwvXv3BqBWrVqcPn0agMzMTGrXrg1oLYz0\n9HTray0WC8HBwdccT09PL9Zi8TQZGdreIAsXOm4GhxBCf507w9at2qZcEyZAZKS2Y6mjOSyJKKV4\n7LHHCAsLY9y4cdbjZrOZuLg4AOLi4jCbzdbjixYtAmDnzp34+PgQFBRE165dSUxMJDs7m+zsbBIT\nE+nWrZujbsOp5OVpTdl//AM89J9ACI/m5QW9e8O+fTBsmFYT7KGHICXFgTGoW+kzKoXNmzfTqVMn\nmjZtildRm+v111+ndevW1im+AQEBLF++3DrFd9SoUWzcuJGKFSsyd+5cIiIiAPjkk0+YMWMGoE3x\nfeSRR4rflIfMznr6afj5Z/j6a9v7RIUQ7uPCBW2d2OXurcmToV69m3+9U0/xdSRPSCJffKFN90tO\nhqJ5CUIIAWi1wN56C2bP1rq7X3wRikYK/paUPfEQv/6qlTVZvlwSiBDiWlWqaK2Qy2VbQkNh4kQt\nudibtEScWF6eVrb64EHtzy+/aH+npGjN1X/8Q+8IhRCuIC1NG4D/5htt5fvIkdoK/KtJd1YRV0si\nf/75vwRxZbJITdVmXDVuDHffrf19+c/NNE2FEOJKKSnw0kuwa5eWVGJjodwVCz0kiRRxxiRSUKB9\nG7gySVz+Oze3eJK4/Dgk5PrfFoQQ4lZs2aK1SE6fhmnTtBldXl6SRKz0TCJnz16bJA4e1Ort+Ptf\nP1kEBsqug0IIx1JKW7T4wgval9V//xs6d5YkAjg+icyZoxVHO3gQ/vpLSw5XJ4tGjaQsuxDC+RQW\nwrJl8PLLcOSIJBHA8Ulk0yZtfvbdd4PBIGs2hBCu59IlqFhRkgjgnGMiQgjh7GSdiBBCCIeSJCKE\nEMJmkkSEEELYTJKIEEIIm0kSEUIIYTNJIkIIIWwmSUQIIYTNJIkIIYSwmSQRIYQQNpMkIoQQwmaS\nRIQQQthMkogQQgibSRIRQghhM0kiQgghbCZJxAUlJSXpHUKZcuf7c+d7A7k/T+SSSSQxMRGTyURY\nWBhvvPGG3uE4nLv/j+zO9+fO9wZyf57I5ZLIxYsXGTlyJImJiezdu5eVK1eya9cuvcMSQgiP5HJJ\nZPv27RiNRoKCgihXrhzR0dHEx8frHZYQQngkl9sed/HixWzatIkPPvgAgKVLl5KUlMSHH35oPcfL\ny0uv8IQQwqWVNiWUK6M4yszNJAgXy4tCCOGyXK47y2AwkJ6ebv05PT2d4OBgHSMSQgjP5XJJpFWr\nVqSkpJCRkUFeXh7Lly8nKipK77CEEMIjuVx3VqVKlfjggw/o0aMHhYWFxMbGEhERoXdYQgjhkVyu\nJQIQFRVFSkoKP//8My+88EKx59x5DUl6ejqdOnXCZDJx9913M336dL1DsruCggLCw8Pp1auX3qHY\n3ZkzZ+jfvz/NmjUjNDSUrVu36h2SXU2cOJFGjRrRuHFj+vXrR05Ojt4h3ZLhw4fj7++PyWSyHvvj\njz/o3r07TZs2pUePHpw5c0bHCG/N9e5v/PjxhIWFERYWRs+ePcnKyirxOi6ZRG7E3deQVKhQgTlz\n5rBv3z6Sk5OZO3cue/bs0Tssu5o5cyZhYWFuOcPuiSeeoE+fPuzZs4f9+/djNBr1Dslufv31VxYu\nXEhKSgoHDx7Ex8eHJUuW6B3WLRk2bBiJiYnFjk2cOJEHHniAvXv3EhUVxcSJE3WK7tZd7/569epl\n/YLepEkTXnvttRKv41ZJxN3XkPj7+9OkSRMAfH19adq0Kb/99pvOUdmPxWIhISGBxx9/3O1m2GVl\nZbF7924GDhwIgLe3N1WqVNE5KvupUaMG5cuX5/z58+Tn55OTk0PdunX1DuuWdOzYkerVqxc7lpCQ\nQGxsLABDhgxx6c+X691f586d8fbW0kKHDh3IyMgo8TpulUQsFkuxmVoGgwGLxaJjRGUnLS2NHTt2\ncM899+gdit2MGzeOGTNmWP8ndieHDx+mVq1aDBgwgCZNmjB06FDOnTund1h2U6NGDSZMmECdOnW4\n8847qVatGt26ddM7LLvLzMzEz88PgJo1a3Lq1CmdIyo7H3/8MQ899FCJ57nVb6s7doFcz7lz5+jf\nvz8zZ86kcuXKeodjF19//TW1a9cmPDzc7VohAIWFhezYsYNnnnmGlJQUatSowZQpU/QOy26OHDnC\nu+++S1paGr/99hvnzp1j0aJFeoclbDR16lQqVKjA4MGDSzzXrZKIJ6whycvLo2/fvgwaNIjevXvr\nHY7dbNmyhTVr1lC/fn0GDhzIhg0bGDp0qN5h2U1wcDBBQUG0atUKgH79+rF7926do7KfH3/8kfbt\n2+Pn50e5cuXo06cPmzdv1jssu6tVqxanT58GtFZJ7dq1dY7I/hYsWEB8fPxNfwlwqyTi7mtIlFI8\n9thjhIWFMW7cOL3Dsatp06aRnp5OamoqS5cupUuXLnz22Wd6h2U3wcHB1KxZk0OHDgGwfv16QkND\ndY7KfkJCQti2bRu5ubkopVi/fj0hISF6h2V3ZrOZuLg4AOLi4jCbzTpHZF+JiYlMnz6dNWvWUKlS\npZt7kXIzCQkJymg0qtDQUDVt2jS9w7GrTZs2KS8vL9WsWTPVvHlz1bx5c/XNN9/oHZbdJSUlqV69\neukdht3t3r1btWzZUoWFhamoqCj1xx9/6B2SXU2cOFGFhISoRo0aqejoaJWbm6t3SLckJiZGBQYG\nqvLlyyuDwaDmz5+vsrKyVLdu3ZTJZFLdu3dXf/75p95h2uzq+5s3b54KCQlRderUsX6+jBw5ssTr\nuFwBRiGEEM7DrbqzhBBCOJYkESGEEDaTJCKEEMJmkkSEEELYTJKIEDfg7e1tLXEBkJ+fT61atWwu\nDvnXX39Zd+QESEpKcstCk8KzSBIR4gbuuOMO9u/fz4ULFwBYt24dBoPB5soIf/75J3PmzLFniELo\nTpKIEH/DbDZbi+wtWbKEgQMHWsuynD59mh49emAymWjRogU7d+4EYNKkSQwfPpxu3bpRt25d3nzz\nTQCef/55jhw5Qnh4OM8++yxeXl6cO3eOmJgYGjVqRP/+/a9b8iUyMpLnn3+e9u3bU79+fTZs2ABA\nbm4uAwcOxGg0YjKZ+Pbbbx3xTyJEMZJEhPgb0dHRLF26lIsXL7Jv3z7atGljfe5f//oXkZGR7Nu3\nj3feeYchQ4ZYnzt8+DBr165l586dTJs2jUuXLvHGG2/QoEEDdu3axfTp01FKsWvXLmbOnMkvv/xC\nRkYG33///TUxXG75bNmyhTlz5vDqq68C8M4771ClShX279/PqlWrePTRR7l48WIZ/4sIUZwkESH+\nhslkIi0tjSVLlvDAAw8Ue+6HH36wlnbv1KkT586d4/Tp03h5eWE2m/H29sbPz4+AgABOnTp13VZG\n69at8ff3x8vLi+bNmxer/Xaly9VUIyIirOdc+f4hISE0bNiQffv22e3ehbgZLrc9rhCO9uCDD/L0\n00/z/fffk5mZWey5GxV8qFChgvWxj48PhYWF1z2vYsWKpTrv6nOufn9PqWQtnIe0RIQowfDhw5k0\nadI1OxF27NiRpUuXArBp0yYqV65MzZo1b5hYbrvtNrtuGduxY0eWLVsGaKXYDx8+bN20TAhHkZaI\nEDdw+Vt9UFAQo0aNsh67fHzq1KkMGjSIJUuWUL58eRYuXHjNOVfy9/enefPmhIWF0atXL8xm8zXn\n3UxL4vI5Y8eOZdiwYRiNRry9vVmwYEGxlo0QjiAFGIUQQthMurOEEELYTJKIEEIIm0kSEUIIYTNJ\nIkIIIWwmSUQIIYTNJIkIIYSw2f8DGJCg9fxyhvcAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x6bdeb10>"
]
},
{
"metadata": {},
"output_type": "display_data",
"text": [
"<matplotlib.figure.Figure at 0x6bde8d0>"
]
}
],
"prompt_number": 116
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But not when using a coord name (or a list of coord name)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"qplt.plot(['month_no'], torn_count)\n",
"plt.figure(num=None)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'list' object has no attribute 'ndim'",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-118-4ff9adcde3e8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mqplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'month_no'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtorn_count\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/project/avd/iris/live/testing/iris/plot.pyc\u001b[0m in \u001b[0;36m_wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 835\u001b[0m \"\".format(coord.name()))\n\u001b[0;32m 836\u001b[0m \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mcoord\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 837\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 838\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_wrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 839\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/project/avd/iris/live/testing/iris/quickplot.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 244\u001b[0m \"\"\"\n\u001b[1;32m--> 245\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0miplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 246\u001b[0m \u001b[0m_label_1d_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 247\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/project/avd/iris/live/testing/iris/plot.pyc\u001b[0m in \u001b[0;36m_wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 835\u001b[0m \"\".format(coord.name()))\n\u001b[0;32m 836\u001b[0m \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mcoord\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 837\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 838\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_wrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 839\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/project/avd/iris/live/testing/iris/plot.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 885\u001b[0m \"\"\"\n\u001b[0;32m 886\u001b[0m \u001b[0m_plot_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 887\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_draw_1d_from_points\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'plot'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_plot_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 888\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 889\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/project/avd/iris/live/testing/iris/plot.pyc\u001b[0m in \u001b[0;36m_draw_1d_from_points\u001b[1;34m(draw_method_name, arg_func, *args, **kwargs)\u001b[0m\n\u001b[0;32m 396\u001b[0m \u001b[1;31m# axes (cubes or coordinates) and their respective values, along with the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 397\u001b[0m \u001b[1;31m# argument tuple with these objects removed\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 398\u001b[1;33m \u001b[0mu_object\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv_object\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_plot_objects\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 399\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 400\u001b[0m \u001b[1;31m# if both u_object and v_object are coordinates then check if a map\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/project/avd/iris/live/testing/iris/plot.pyc\u001b[0m in \u001b[0;36m_get_plot_objects\u001b[1;34m(args)\u001b[0m\n\u001b[0;32m 373\u001b[0m \u001b[1;31m# two arguments\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 374\u001b[0m \u001b[0mu_object\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv_object\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 375\u001b[1;33m \u001b[0mu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_uv_from_u_object_v_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 376\u001b[0m \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 377\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/project/avd/iris/live/testing/iris/plot.pyc\u001b[0m in \u001b[0;36m_uv_from_u_object_v_object\u001b[1;34m(u_object, v_object)\u001b[0m\n\u001b[0;32m 347\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_uv_from_u_object_v_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mu_object\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv_object\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 348\u001b[0m \u001b[0mndim_msg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Cube or coordinate must be 1-dimensional. Got {} dimensions.'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 349\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mu_object\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mu_object\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 350\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mndim_msg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mu_object\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 351\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mv_object\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'ndim'"
]
}
],
"prompt_number": 118
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"qplt.plot('month_no', torn_count)\n",
"plt.figure(num=None)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'str' object has no attribute 'ndim'",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-119-2e24e38b4377>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mqplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'month_no'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtorn_count\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/project/avd/iris/live/testing/iris/plot.pyc\u001b[0m in \u001b[0;36m_wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 835\u001b[0m \"\".format(coord.name()))\n\u001b[0;32m 836\u001b[0m \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mcoord\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 837\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 838\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_wrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 839\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/project/avd/iris/live/testing/iris/quickplot.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 244\u001b[0m \"\"\"\n\u001b[1;32m--> 245\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0miplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 246\u001b[0m \u001b[0m_label_1d_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 247\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/project/avd/iris/live/testing/iris/plot.pyc\u001b[0m in \u001b[0;36m_wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 835\u001b[0m \"\".format(coord.name()))\n\u001b[0;32m 836\u001b[0m \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mcoord\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 837\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 838\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_wrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 839\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/project/avd/iris/live/testing/iris/plot.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 885\u001b[0m \"\"\"\n\u001b[0;32m 886\u001b[0m \u001b[0m_plot_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 887\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_draw_1d_from_points\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'plot'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_plot_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 888\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 889\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/project/avd/iris/live/testing/iris/plot.pyc\u001b[0m in \u001b[0;36m_draw_1d_from_points\u001b[1;34m(draw_method_name, arg_func, *args, **kwargs)\u001b[0m\n\u001b[0;32m 396\u001b[0m \u001b[1;31m# axes (cubes or coordinates) and their respective values, along with the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 397\u001b[0m \u001b[1;31m# argument tuple with these objects removed\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 398\u001b[1;33m \u001b[0mu_object\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv_object\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_plot_objects\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 399\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 400\u001b[0m \u001b[1;31m# if both u_object and v_object are coordinates then check if a map\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/project/avd/iris/live/testing/iris/plot.pyc\u001b[0m in \u001b[0;36m_get_plot_objects\u001b[1;34m(args)\u001b[0m\n\u001b[0;32m 373\u001b[0m \u001b[1;31m# two arguments\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 374\u001b[0m \u001b[0mu_object\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv_object\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 375\u001b[1;33m \u001b[0mu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_uv_from_u_object_v_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 376\u001b[0m \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 377\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/project/avd/iris/live/testing/iris/plot.pyc\u001b[0m in \u001b[0;36m_uv_from_u_object_v_object\u001b[1;34m(u_object, v_object)\u001b[0m\n\u001b[0;32m 347\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_uv_from_u_object_v_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mu_object\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv_object\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 348\u001b[0m \u001b[0mndim_msg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Cube or coordinate must be 1-dimensional. Got {} dimensions.'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 349\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mu_object\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mu_object\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 350\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mndim_msg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mu_object\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 351\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mv_object\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAttributeError\u001b[0m: 'str' object has no attribute 'ndim'"
]
}
],
"prompt_number": 119
}
],
"metadata": {}
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment