Twin axis of vertical profile

2015/10/22 ROMS NetCDF, Vertical

{ "cells": [ { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Pataoyb0D7+XuL+6mVEtdh5M0gU8E06fDySe7jsKkBbt01CTBsE7DqFerHq9/\n/brrUJIm8Ilgxgzo29d1FCYtWI/AJIGIcN9Z93HP5Hso2V/iOpykCHQi2LcP5syBPn1cR2LSgvUI\nTJIMaDuAdo3b8ey/nnUdSlIEOhEsXAjHHgsNG7qOxASeXTpqkuxPA//E2Klj2bV3l+tQfBfoRDBj\nhp0fMDGyS0dNkvVu3pvTWp3GYzOCO1VEopKWCETkZhFZLCJfi0j0qfyqMH26nR8wMVq2zM4PmKT7\n44A/8pev/sKW3Vtch+KrpCQCERkADAW6qWoe8OdEtmNXDJmYrVzp1TI1Jok6HdWJIe2H8I95/3Ad\niq+S1SO4AbhPVfcCqOoP8W5gyxZv1oEuXXyPzfjJVR2C8vtdswbatIl/O8bE6YYTb+Dvc/5O+Zth\nq6olkEiNgpTVJ/B7XutQ48wFxgDTgULgxAqWq3DO7YkTVfv1q3JqbuOaqzoE5fd76aWqr70W3zaM\nSUBpaal2faKrfrHyi4Ner6yWQKI1CqJtkyDVIxCRSSKyMMpjKN4cRo1U9WTgd8Ab8W7fhoUyVLLq\nEKxeDa1bVzs8Y6oiIlzf+3qenPNkzOsEvUZBwpPOqeqgit4TkRuAd0LLzRKRUhE5UlU3lV92TEQX\nv3///vTv3x/wrhj61a8Sjc6kVBDqENjQkEmhn3X7GaMnj2bjzo00bdC07PXKppFOtEZBYWEhhYWF\n1Yq3KkmZdE5Ergeaq2q+iHQEPlPVY6Msp9H2rwpHHeXNOnrMMb6HZ/wkEn8dgkWLDq1D0LlzfHUI\nIve7ezc0agS7dkGNQF8RbTLIteOvpX3j9tx5+p2AlwQqqlWQe38ui25cdEiNgs5PdK60RkG0babT\npHPPAceJyELgNeDqeFZetgwaNLAkkJGSUYdgzRpo1cqSgEmpX5/4a56a81RMk9EFvUZBUuoRqHe1\n0H8luv6cOXDSST4GZJLHVR2CyP3asJBx4MTmJ9KoXiMmrZjEOe3PqbSWQKI1ClJVnyCQ9QgKCrwZ\nA8aOdRCUST9//zvMmgXPPOM6EpNlnp7zNB8v/5h3L383ZftMp6GharG5w0xcrEdgHBnedThTVk8p\nG/JJV5YITPpbs8YuHTVONKjTgCvyruDZuek9K2ngEoEqLF1qicDEwe4hMA5d3/t6nv7X0+wr3ec6\nlIQFLhFs2nTg8lFjYmJDQ8ah7s260zKnJROWTXAdSsIClwjCw0LVubfIZJGSEvjhB2je3HUkJov9\nuvev47oS+ns2AAAZ00lEQVTTOGgCmQg6dnQdhUkb69Z5SaBWUq6ENiYml3W5jBlFM1izdY3rUBIS\nyERg5wdMzNau9crYGeNQvdr1uCLvCl5Z+IrrUBJiicCkt+LiQ+cuMsaBC4+/kPeXvO86jIQELhHY\nFUNpJN5aBMmoQ7B+vZ0fMIFwZuszWbZpGet3rD/o9cpqCsRaoyDpdQn8ntc6ngfl5pQvLVVt0EB1\n8+ZKp+g2QRFPLYJk1SG45RbVhx+ObxvGJMmVb1+pT8568qDXKqpTEE+NgshtEKR6BMmwcSPUretN\nJGkyTLLqENjQkAmQYZ2GxTw8FKQaBYG61MLOD6SheK7zTUYdgvXrLRGYwBjSfgjXjr+WHXt2cETd\nI8per6jmQKI1CvwWuERgl46mmVgnLczNPfSv9+Jib/bReOsQRCoutnMEJjBy6uZwaqtT+WTFJ1zS\n+ZKy16PVKci9P5fi7cWH1CjIqZtzSI2CZCeGQA0NWY8ggyWjDkFpKWzYYInABEqsw0NBqlEQqB7B\nihVw4YWuozAxi6cWQTLqEPzwg7edunXj24YxSTS001BGTx7N3v17qV2zdoU1BeKpUZDsugSBqkdw\nySVwxRXev8ZUae5c+PnPYf5815EYc5CTnj6JB89+kAFtB/i+7YyvR3DYYV75WWNiYlcMmYCK5+qh\nIAhUIqhXzxKBiYMlAhNQ4UTgcsQlHoFLBD/+6DoKkzYsEZiAymuShyAs/H6h61BiEqhEYENDJi42\nvYQJKBHxegXfpsfwUKASgfUITFysR2ACbNjx6XOeIFCJwHoEJi6WCEyAnX7s6azeupp129a5DqVK\nSUkEItJDRKaLyFwRmSUiJ8Wynp0sNnGxu4pNgNWqUYvzOpzH+CXjXYdSpWT1CB4E8lW1J3BP6Ocq\n2dCQidmPP8LOnVbc2gTasE7DGL80exNBKZAbet4QKI5lJRsaSiOprkVQfn8bNkDTplAjUKObxhxk\nULtB/N+6/+PHfQf+wq2otkAstQmSVpfA73mtQ9fNHg+sAdYCRUCrCpY7aM7tceNUL7kk6tTdJmhS\nXYug/P6mT1c98cTY1zfGkb5P99XJqyaX/RytPkGstQkYQ7DqEYjIJBFZGOUxFLgRuFVVjwVuA56L\nZZs2NJShklGLYMMGaNbMn/iMSaKBbQfyxaovKl3GdW2ChCedU9VBFb0nIi+qangKvbeAZypadkxE\nl79Bg/7s3t0/0ZBMqrmsRbBxozc0ZEzADWw7kDGFY/jDgD+UvRZtWmmXtQmSNfvoehHpp6pTgIHA\n0ooWjEwE06bBe+8lKSLjv1TWIiifMDZutB6BSQuntjqVeRvmsbNkJw3qNAAOrU8Qa22CZCWFZJ1p\nuw74i4jMA8YCv4plJTtZnKGSUYsgfLLYmICrX7s+JzY/kWlrp1W4jOvaBEnpEajql8CJ8a5n9xGk\nkVTXIii/v40boX//2Nc3xqHweYIh7YdErS0Qa22C/H75FFDge3yBqkewciWcfbb3rzGVOv10+NOf\n4MwzXUdiTJWmrZ3GrRNvZfavZld7W1aPwJgwO0dg0kifFn1YumkpW3ZvcR1KVIFLBHb5qImJnSMw\naaROzTqc2upUpqyZ4jqUqAKVCPbvh1qBqqJsAuk//4F9+7xzDcakiVjuJ3AlUImgpATq1HEdhQm8\n8D0Eid6DYIwDlghitHcv1K7tOgoTeHYzmUlDPZv1pHhHMRt3bnQdyiEClQisR2BiYieKTRqqWaMm\n/Vr3Y/Lqya5DOYQlApN+7ESxSVNBHR6yRGDSj/UITJqyRBADSwRpItW1CMrv03oEJk11OboLO0p2\nsGbrmrLXytcYiKUugd8sEZj4FcRxi/vo0XDvvbBokTdJ3aJFMHZs/Mkgcp/WIzBpSkQY0GbAQecJ\nCqYcOLZHfzGae6fey6IbF6H5yqIbFzF26tikJwNLBCa5klWLwHoEJk1VNjzkqi5BoG7fskSQRlzX\nIrAegUlTA9sO5A9T/oCqIqHfgcjppV3UJbAegUmMVzyy6kdOzoHpp8PCtQhi3Ub5iRG//x6OPjp1\nn9UYH7Vr1I5SLWXNtgPnCTRf0Xwlp25O2VTUYeG6BOFlksESgUkuv2sR7N3rzUxo00uYNCUi9Dym\nJ/M3zD/kPVd1CWxoyMQv1bUIIve5dSs0bGjTS5i01qNpD+ZtmMew44cdVJ8g1roEfgtUPYLnnvPK\nVT4XU6l7k5WWLIHzz4elFVY/NSbw3vzmTV79+lXevfzduNfN+HoE1iMwVdqyBRo1ch2FMdXSo5nX\nIwgKSwQmvVgiMBmgXeN2/HvXv9n641bXoQABSwR791oiMFXYvBkaN3YdhTHVUkNq0K1pt6gnjF0I\nVCIoKbFpqE0VrEdgMkT4hHEQBC4RWI/AVMoSgckQPZr1YN5GSwSHsERgqmRDQyZDBOmEccKJQEQu\nFZFvRGS/iPQq995dIrJMRL4VkcGxbtMSgamS9QhMhshrkseSfy+hZH+J61Cq1SNYCFwI/DPyRRHp\nDFwOdAaGAE+ISEz72b/f7hMyVbBEYDJEvdr1aNuoLYt/WOw6lMQTgap+q6rR7uoZBrymqntVdTWw\nHOgTyzZbtoS1axONyKRMqusRRO5v82ZLBCZjRA4PuaxLkIxzBM2Booifi4AWFSx7kA4dYPnyJERk\n/JXqegSR+9uyxc4RmIzRo2kP5m6YC7itS1BpIhCRSSKyMMrj/Dj3E9M8Fh06wLJlcW7ZBJvf9Qhs\naMhkkIpOGKe6LkGlk86p6qAEtlkMtIr4uWXotajGRHT7Tz21P2vX9mfvXrufIPBc1SOwoSGTQbo3\n6868DfMIz7kWS12CZPBr9tHI3+jxwKsi8jDekFAHYGZFK44pN97cvDmsWQPt2/sUmUmOWCcrzM31\npp6OTAbhegTbtsW2jXDC+PFHKC2F+vXji9WYgGpyeBMOr3N4WW2CcL2B3PtzKd5efFAyCNcl2I7/\nyaA6l49eKCLrgJOBj0RkAoCqLgLeABYBE4AbNY4pTm14KMP4WY8gPCxkl5aZDBJteCjVdQkS7hGo\n6rtA1DlUVfVPwJ8S2W779l4iOPfcRCMzSZfqegTh/dmwkMlA4akmYq1LMJaxvscQqHoEAI88AqtW\nVa+2uclQ06bBHXfAl1+6jsQY37z5zZu8svAV3rvivZiWz/h6BHCgR2DMIeyKIZOBgjDVROASgZ0j\nMBWyoSGTgdo1bsem3ZvYsnuLsxgClwjatoV167zaBMYcxG4mMxmorDbBRne1CQKXCOrW9S4hXb3a\ndSQmcGxoyGQo17UJApcIwIaHTAV27bJ7CExGapnTkg07Nzjbf2ATgc05ZA5htUxNhso9LJdtP8Z4\ng2USBDIR2JVDJiqrZWoyVG7dXLbtsURwEBsaMlFZ5SKToXIPs0RwCBsaCjCXtQhsaMhkqNy6B4aG\nIusSpKwmgao6e3i7P9SePap16qiWlER927hUwf9ZVKNGecsXFXk/FxV5P48endj+rrhC9ZVXYl/X\nmDQxf8N8zXsiT1VVGeMd86M+H6WMQYu2eb8/RduKlDFo6HvT1+/iQPYI6tTxJqtctcp1JKZa/K5F\nYD0Ck6EiewRhFdUkSAa/pqH2XXh4qGNH15GYQ7iqRWDnCEyGKn+OIFyXIFpNgmQIZI8A7MqhQPMG\nbKp+5OQcmH46LFyLINZtRLKrhkyGOqLOEews2UmplgJeXYKcujll01CHlf/ZL4FNBHblUAbwsxYB\n2NCQyVg1a9Tk8NqHs2PPjrLXKqpJkAyBHhqaONF1FOYQrmoRgA0NmYwWHh4K1yWoqCZBVtQjCFuy\nBM47D1asSHFQJrj69oVHH4WTT3YdiTG+y3sij9cufo2uTbtWulxW1CMIa9vWu8CkpMR1JCYwbGjI\nZDCXN5UFNhFs2uTNL2blaU0ZGxoyGSzaJaSpEthE8OGHMGSIXSRiIthVQyaDWY8givHjYehQ11GY\nQLGhIZPBrEdQzn/+A1OmeD0CY8rY0JDJYC5nIA1kIvjsMzjpJCtGZcqxoSGTwVzWJKhWIhCRS0Xk\nGxHZLyK9I14fJCKzRWRB6N8B8WzXhoVMVDY0ZDJYOvcIFgIXAv8EIm8I+AH4qap2A64BXop1g/v3\neyeKzz+/mpGZzGNDQyaDpe3JYlX9VlWXRnl9nqqGC3AuAuqJSEx9+pkzoUkTOO646kRmkiLWWgSJ\n1iCoavs2NGQyWPhksYt6BKk4R3AxMEdV98aysA0LBVhBQdXLjB4N994LixZ5k8YtWgRjx8aWDCrb\nfmmp112sFdhZUYyplnCPoGCK93sw+ovR3Dv1XhbduAjNVxbduIixU/2fXgJiSAQiMklEFkZ5VDl4\nIyJdgPuB62MNyBJBmvO7BkFY+PyA3WFoMlT5y0cDVY9AVQclsmERaQm8A/yXqlZYYmZMxHBAhw79\n2bSpPyedlMgeTUrE8kXsZw2CMBsWMhku8hxBqusR+NnPLvstF5GGwEfAHar6VWUrRSaCRx7xThLX\nCORFrQY4tEZAebm53nTTkckgXINgWxUnwipLFHbFkMlwkT0CzVdy78+leHvxQV/+gaxHICIXisg6\n4GTgIxGZEHrrJqAdkC8ic0OPo6rang0LZQC/axCEWY/AZLicujnsKEnDegSq+i7wbpTXx0J8k2Zv\n3gxz5sBZZ1UnIpNUsdQiqE4Ngsq2b5eOmgxXs0ZN6teuz00n3QRkaT2CV16BN96A9993Fo4JsvXr\n4bbbYNw415EYkzQXjbuIp85/iqPqVzyAkox6BIFJBJdfDoMHw4gRzsIxxpjAy9hEUFLi3US2ZAk0\nbeosHGOMCbyMrVA2ZQqccIIlAWOMcSEQicCuFjLGGHec36+v6iWCjz92HYkxxmQn5z2CBQu86WM6\nd3YdiTHGZCfniSA8LGRTyBhjjBuBSQTGGGPccH75aKNGysaNNnuAMcbEIiMvHz33XEsCxhjjkvNE\nYMNCxhjjlvNEMGSI6whMda1bt44BAwbQpUsX8vLyeCyiCM3mzZsZNGgQHTt2ZPDgwWzdurXs9QED\nBnDEEUdw8803ly2/e/dufvKTn3DCCSeQl5fHXXfdVeF+58yZQ9euXenQoQMjR44se/3555/n6KOP\npmfPnvTs2ZPnnnsu6vr//Oc/6dWrF7Vr1+btt98ue33y5Mll6/bs2ZN69eoxfvz4Q9av6LMBLFiw\ngFNOOYW8vDy6devGnj174lr/vvvuo0OHDhx//PF8+umnUeOv7vrGlFFVZw9v9ybdfffddzp37lxV\nVd2xY4d27NhRFy9erKqqv/vd7/SBBx5QVdX7779f77jjDlVV/c9//qPTpk3TJ598Um+66aaybe3a\ntUsLCwtVVbWkpETPOOMMnTBhQtT9nnTSSTpjxgxVVT333HPLlnv++ef15ptvrjLu1atX64IFC/Tq\nq6/Wt956K+oymzdv1saNG+vu3bsPea+iz7Z3717t1q2bLliwoGwb+/fvj3n9b775Rrt3764lJSW6\natUqbdeuXVLWN+kp9L3p63ex8x6BSX/NmjWjR48eADRo0IATTjiB4lA9gvHjx3PNNdcAcM011/De\ne+8BUL9+fU477TTq1q170Lbq1atHv379AKhduza9evUq21ak7777jh07dtCnTx8Arr766rJt64E/\nNCrVunVrunbtSo1KKiG9+eabnHfeeRx22GGHvFfRZ/v000/p1q0bXbt2BaBRo0ZR91HR+u+//z7D\nhw+ndu3atGnThvbt2zNz5kzf1zcmzBKB8dXq1auZO3cuffv2BWDjxo00DU0i1bRpUzZu3HjQ8lLJ\nDSRbt27lgw8+4KwoRSqKi4tp2fJAkY4WLVqUJQwR4e2336Zbt25ceumlFBUlXuf19ddfZ/jw4VHf\nq+izLV26FBFhyJAh9O7dm4ceeqhsneuuu45//etfla6/fv36gz5by5YtWb9+fbXWj5ZMjQlzPsWE\nyRw7d+7kkksu4dFHH6VBgwaHvC8ilX7xR9q3bx/Dhw9n5MiRtGnTJq44zj//fK688kpq167NU089\nxTXXXMPnn38e1zbA63V8/fXXnHPOOVUuG/nZ9u3bx7Rp05g9ezb16tXjrLPOonfv3gwcOJCnn366\nyvUrk+j6sba7yU7WIzC+2Lt3LxdffDE/+9nPuOCCC8peb9q0KRs2bAC8L9YmTZrEtL1f/epXdOrU\niVtCJS73799Pjx496NmzJ2PGjKFly5YH/aVfVFREi1Cd5MaNG1M7dE3yiBEjmDNnDgCjRo2iZ8+e\n9OrV65D9RfuifOONN7jooouoWbNm1Bgr+mytWrXizDPPpHHjxtSrV4/zzjuv7K/4WNZv0aIF69at\ni/rZ/FzfmDBLBKbaVJURI0bQuXNnbr311oPeGzp0KC+88AIAL7zwwkFJIrxueaNHj2b79u088sgj\nZa/VrFmTefPmMXfuXMaMGUOzZs3IyclhxowZqCovvfRS2bbDX47gjaN3Dk1kde+99zJ37txDvpQr\nOqfw2muvVTgsVNlnGzx4MAsXLmT37t3s27ePKVOm0KVLl5jXHzp0KK+//jolJSWsWrWKZcuWlZ0L\n8XN9Y8r4ffY5ngd21VBGmDp1qoqIdu/eXXv06KE9evQou4Jn06ZNetZZZ2mHDh100KBBumXLlrL1\nWrdurY0bN9YGDRpoy5YtdfHixbpu3ToVEe3cuXPZtp599tmo+509e7bm5eVpu3btDrpK6K677tIu\nXbpo9+7ddeDAgbpkyZKo68+cOVNbtmyphx9+uB555JGal5dX9t6qVau0ZcuWlX7uyj7byy+/rF26\ndNG8vLyyq3lUVa+99lqdPXt2levfe++92q5dO+3UqZNOnDjRt/VN+iMJVw05n2LC5f6NMSbdZOQU\nE8YYY9yyRGCMMVku4UQgIpeKyDcisl9EDrkMQ0SOFZGdInJ79UI0xhiTTNXpESwELgT+WcH7DwMf\nVWP7WaWw0HUEwWDtcIC1xQHWFsmVcCJQ1W9VdWm090TkAmAlsCjR7WcbO9A91g4HWFscYG2RXL6f\nIxCRBsB/A2P83rYxxhj/VTrFhIhMAppFeetuVf2ggtXGAI+o6i6x+9qNMSbwqn0fgYhMBm5X1X+F\nfv4n0Cr0dkOgFPi9qj4RZV27icAYY+Lk930Efk06VxaUqp5Z9qJIPrAjWhIILWs9BmOMcaw6l49e\nKCLrgJOBj0Rkgn9hGWOMSRWnU0wYY4xxz7erhkQYIsK3IiwT4Y4Klnks9P58EXpWta4IjUWYJMJS\nET4VoaFf8SZTktriIREWh5Z/R4TcVHyW6kpGW0S8f7sIpSI0TuZn8Euy2kKEm0PHxtciPJDsz+GH\nJP2O9BBhughzRZglwkmp+CzVVc22eE6EjSIsLLd8fN+dfsxcB1oTdDloG9DaoPNATyi3zHmgH4ee\n9wWdXtW6oA+C/nfo+R2g9/s9657fjyS2xSDQGqHn92dzW4TebwU6EXQVaGPXn9XhcTEAdBJo7dDP\nR7v+rA7b4lPQc0LPzwWd7PqzJrMtQj+fAdoTdGG5deL67vSrR9AHWK7KalX2Aq8Dw8otMxR4AUCV\nGUBDEZpVsW7ZOqF/D57MPpiS0haqTFKlNLT+DKAlwZes4wK8O9f/O9kfwEfJaosbgPtCr6PKD8n/\nKNWWrLYohbKeckMgHepzVqctUGUqsCXKduP67vQrEbQA1kX8XBR6LZZlmleyblNVwkVuNwJNfYo3\nmZLVFpF+CXxc7UiTLyltIcIwoEiVBX4HnETJOi46AGeGhkQKRTjR16iTI1ltcSvwkAhrgYeAu3yM\nOVmq0xaVieu7069EEOsZ51guF5Vo21NF49iPS362xaErCaOAElVeTWT9FPO9LUSoB9wN5CeyvkPJ\nOi5qAY1UORn4HfBGnOu7kKy2uBG4VZVjgduA5+Jc34VE2yLm78JYvjv9SgTFHLiJjNDzoiqWaRla\nJtrr4S7dxnAXSIRjgO99ijeZ/GyLg9YV4efAecBV/oWbVMloi3ZAG2C+CKtCy88RIbZiyO4k67go\nAt4BUGUWUCrCkf6FnRTJaourVXk39PwtvGGXoEu0Laoa9orvu9OnEx61QFeETnjUieGEx8kRJ38q\nXDd0wuOO0PM70+QEabLaYgjoN6BHuf6Mrtui3PrpcrI4WcfF9aAFoecdQde6/qwO22IRaL/Q87NA\nZ7n+rMlsi4j32xD9ZHHM351+fqBzQZfgnQG/K+IgvT5imb+G3p8P2quydUOvNwb9DHQp3hUBDV3/\nxzlsi2Wga0Dnhh5PuP6crtqi3PZXpkMiSOJxURv0JdCFoHNA+7v+nA7b4jTQ2aEv069Ae7r+nClo\ni9dA14PuAV0H+ovQ63F9d9oNZcYYk+WsVKUxxmQ5SwTGGJPlLBEYY0yWs0RgjDFZzhKBMcZkOUsE\nxhiT5SwRmKwlwgWhaaw7hX5uEzmdrwjXiTA7Xab8NiZRlghMNhsOTA39exAR/gu4CRisyrZUB2ZM\nKlkiMFlJhAbAacC1wBXl3rsMuAMYpMp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"text/plain": [ "<matplotlib.figure.Figure at 0x17b7ce10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# (c) 2015 Teruhisa Okada\n", "\n", "% matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "import netCDF4\n", "from datetime import datetime\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import romspy\n", "\n", "stafile = 'Z:/roms/Apps/OB500_fennelP/NL12/ob500_avg.nc'\n", "obsfile = 'F:/okada/Dropbox/Data/ob500_obs_2012_obweb-4.nc'\n", "stations = 'F:/okada/Dropbox/Data/stations13.csv'\n", "\n", "nc = netCDF4.Dataset(stafile, 'r')\n", "obs = netCDF4.Dataset(obsfile, 'r')\n", "\n", "\n", "def station2xy(station):\n", " df = pd.read_csv(stations)\n", " x = df.xgrid[station-1]\n", " y = df.ygrid[station-1]\n", " return x-1, y-1\n", "\n", "def vplot(month, station):\n", " mod_time = nc.variables['ocean_time'][:]\n", " mod_time = netCDF4.num2date(mod_time, romspy.JST)\n", " t = np.where(np.asarray([t.month for t in mod_time])==month)[0][0]\n", " x, y = station2xy(station)\n", " akv = nc.variables['AKv'][t,:,y,x]\n", " rho = nc.variables['rho'][t,:,y,x]\n", " temp = nc.variables['temp'][t,:,y,x]\n", " salt = nc.variables['salt'][t,:,y,x]\n", " chlo = nc.variables['chlorophyll'][t,:,y,x]\n", " DO = nc.variables['oxygen'][t,:,y,x]\n", " cs_r = nc.variables['Cs_r'][:]\n", " cs_w = nc.variables['Cs_w'][:]\n", " h = nc.variables['h'][y,x]\n", " zeta = nc.variables['zeta'][t,y,x]\n", " depth = cs_r[:] * (h + zeta)\n", " depth_w = cs_w[:] * (h + zeta)\n", " \n", " \"\"\"fig, ax = plt.subplots(1, 2, figsize=[12,4])\n", " axes = [ax[0], ax[0].twiny(), ax[0].twiny()]\n", " axes1 = [ax[1], ax[1].twiny()]\"\"\"\n", " fig, ax = plt.subplots(1, 1, figsize=[6,4])\n", " axes = [ax, ax.twiny(), ax.twiny()]\n", " \n", " #fig.subplots_adjust(right=0.75)\n", " axes[-1].spines['top'].set_position(('axes', 1.2))\n", " axes[-1].set_frame_on(True)\n", " axes[-1].patch.set_visible(False)\n", "\n", " axes[0].plot(akv, depth_w, 'b-')\n", " axes[0].set_xlabel('AKv', color='b')\n", " axes[0].tick_params(axis='x', colors='b')\n", " axes[0].set_xlim(0,0.01)\n", " \n", " axes[1].plot(temp, depth, 'r-')\n", " axes[1].set_xlabel('temp', color='r')\n", " axes[1].tick_params(axis='x', colors='r')\n", " axes[1].set_xlim(5,30)\n", " #axes[1].plot(rho, depth, 'r-')\n", " #axes[1].set_xlabel('rho', color='r')\n", " #axes[1].tick_params(axis='x', colors='r')\n", " #axes[1].set_xlim(15,25)\n", " \n", " axes[2].plot(salt, depth, 'g-')\n", " axes[2].set_xlabel('salt', color='g')\n", " axes[2].tick_params(axis='x', colors='g')\n", " axes[2].set_xlim(22,32)\n", "\n", " \"\"\"axes1[0].plot(chlo, depth, 'b-')\n", " axes1[0].set_xlabel('chlorophyll', color='b')\n", " axes1[0].tick_params(axis='x', colors='b')\n", " axes1[0].set_xlim(0,20.0)\n", " \n", " axes1[1].plot(DO, depth, 'r-')\n", " axes1[1].set_xlabel('DO', color='r')\n", " axes1[1].tick_params(axis='x', colors='r')\n", " axes1[1].set_xlim(0,300.0)\"\"\"\n", " \n", " axes[2].text(27, -13.5, datetime.strftime(mod_time[t], '%Y-%m-%d %H:%M:%S'), ha='center')\n", " \n", " start = netCDF4.date2num(datetime(2012,month,1,0), romspy.JST_days)\n", " if month == 12:\n", " end = netCDF4.date2num(datetime(2013,1,1,0), romspy.JST_days)\n", " else:\n", " end = netCDF4.date2num(datetime(2012,month+1,1,0), romspy.JST_days)\n", " obs_time = obs.variables['obs_time'][:]\n", " obs_station = obs.variables['obs_station'][:]\n", " index = np.where((start<=obs_time) & (obs_time<end) & (obs_station==station))[0]\n", " if len(index) > 0:\n", " obs_type = obs.variables['obs_type'][index]\n", " obs_depth = obs.variables['obs_depth'][index]\n", " obs_value = obs.variables['obs_value'][index]\n", " data={'time':obs_time[index], 'depth':obs_depth, 'type':obs_type, 'value':obs_value}\n", " df = pd.DataFrame(data)\n", " varid = {'temp':6, 'salt':7, 'chlorophyll':10, 'oxygen':15}\n", " for d in range(0, -15, -1):\n", " df2 = df[df.depth==float(d)]\n", " temp = df2[df2.type==varid['temp']]\n", " salt = df2[df2.type==varid['salt']]\n", " chlo = df2[df2.type==varid['chlorophyll']]\n", " DO = df2[df2.type==varid['oxygen']]\n", " axes[1].errorbar(temp.value.mean(), d, xerr=temp.value.std(), fmt='o', ecolor='r', mec='r', mfc='w', mew=1)\n", " axes[2].errorbar(salt.value.mean(), d, xerr=salt.value.std(), fmt='o', ecolor='g', mec='g', mfc='w', mew=1)\n", " \"\"\"axes1[0].errorbar(chlo.value.mean(), d, xerr=chlo.value.std(), fmt='o', ecolor='b', mec='b', mfc='w', mew=1)\n", " axes1[1].errorbar(DO.value.mean(), d, xerr=DO.value.std(), fmt='o', ecolor='r', mec='r', mfc='w', mew=1)\"\"\"\n", " \n", "vplot(5, 12)\n", "plt.savefig('profile_obs_avg_AKv.png', bbox_inches='tight', dpi=300)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

Teruhisa Okada

Twin axis of vertical profile

Related figures

Time-depth of observations

Vertical view for ROMS output

Horizontal view