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gremau/Mojave_daycent_validation.ipynb

Last active June 9, 2017 16:51
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Figures tables of DayCent carbon and trace gas output for the Mojave region
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Mojave_daycent_validation.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 176,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('../../ecoflux')\n",
"import pandas as pd\n",
"import iodat as ld\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import datetime as dt\n",
"import pdb as pdb\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Soil respiration\n",
"\n",
"## Parse Mojave background data"
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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7zUZ+5mn0RiOhLaK9bth2jVr2Qoi5QogzQoi0SuvChRA/CSEOuB/D3OuFEOJN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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe17ef149e8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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VyR6C3bniUszojVoqil1UuxqO7dqZWlLP/oYbboj4o3ql8xQdriR3dR6Z56ST2i/u9Ct0\nMTE2I3PmjyWlTywfv7SV7d81qJmotIOo6HrZFI1GEJ9ipqzARXmRi4QeZvTG8HwlVc9eaY1AQLL6\n9Z3B0admDQx3OGFjsuqZdfcYPn5xK1/+ayfuKi9jL+wX7rC6lKg9sq+h0WqITzWj0QrKCl34qtXw\nhkr0qBl96qy5kTP6VLjojVouvnU0Q8ansmbZPr57b2+ndrHu6qI+2QNodRoSUi0IISgrdOLzqoSv\nRL7a0aeGJjB0YmSNPhUuWp2Gqb/KIHNKOjmfHuaLJTtUqfN20iWSPYBWryEh1YyUUFboUqWRlYi3\nJjT61DkROvpUuAiNYPJVQ5lw6QB2rjnOf1/MVWfs7aDLJHsIVtlLSLUg/ZKyQme7HBEcPHiQUaNG\ntXr9/v37U1xc3GD673//e5588sl2i2fx4sXcfvvtrYpR6XzH9pSy84fjZE/tS2Ja5I4+FS5CCCZe\nOoBzrhrKwa3FvP/cZjxh7oQR7bpUsodgu198qlmVRlYilt8fYPUbu4lNNDE+CkafCqfMc3sz7VcZ\nHN9XzvIFG3FWdN6d811Nl0v2AAaTjvhkE75qP+VFznarob1//37GjBnDjz/+yPz588nMzGT06NE8\n99xzp1zviSeeYOLEiUycOJG9e/c2mP/yyy8zYcIEsrKymDt3Lk5n8PbxgoICZs+eTVZWFllZWXz/\n/feNxlNTjuHIkSNMnz6dYcOG8cgjj7TLd1baX83oU5Ovip7Rp8JpyIQeXHzbaMoKnCx9YgMVxa5w\nhxSVoqbr5WNrH2OnfWeL1qkpiyy0Ap2+4X5teOJw7p94f7O21ZoSxzXi4uJYu3YtS5Ys4e677+aD\nDz6oN3/OnDnceOONAPzud7/jlVde4Y477uDOO+9kypQpLFu2DL/fT1VVFaWlpQ3iyc7OZtu2baps\nchSotLtZ98EB+o9OZkCUjT4VTv0ykph19xg++Ntm3ntiAzPvzCYpPSbcYUWVLnlkX0OjFWj1GmQo\n6bdWTYnjf//732RnZ/PZZ581q8Rxjauvvrr2ec2aNQ3m5+bmMnnyZDIzM3n99dfZtm0bAF988QW3\n3HILAFqtlvj4+EbjqVFTNtlsNteWTVYiy7dv7wEJk6N09Klw6jkwntnzxyKAZU9tJH9febhDiipR\nc2Tf3CPwxjjKPTjKPJhjDcTYjC3u+VC3xHFGRkazSxzXqLtsY+vdcMMNLF++nKysLBYvXszq1atb\nFE9T21Y9PCLLwa3F7M8p4ozLBhKXHL2jT4VTUq8Y5tw3jpXP5rBy4SYunDeK/pnqDKk5uvSRfQ1L\nnAFzqDSysxWlkVtb4rjGW2+9Vft85plnNphfWVlJWloaXq+X119/vXb6+eefz6JFiwDw+/1UVFQ0\nGk8NVTY5cvm8fr55aze2nhayL+gb7nCiWlyymTnzx2FLs/LfRVvZ9ePxcIcUFbpFshdCEGMzYrLq\ncZR7WnVFvzUljmt4PB4mTZrEM888w9NPP91g/h//+EcmTZrE1KlTGT58eO30Z555hi+//JLMzEzG\njRtX27xzcjwrVqwAVNnkSHZ8fwUVxW7OmDWoS40+FS6WOAOX3TOGtCHxfPbadjZ/cSTcIUW8qCxx\n3FrRXhq5s6kSx+1n6+o8vv7Pbm549CysCcZwh9Nl+Lx+Pn1lO/tzihh/cX8mzhjQ7Zovu3SJ49Y6\nuTSyR5VGVjqJPd+BwazDEq8OMNqTTq/lwhszGHFWGus/OshXb+5W99Y0IWou0LaXmtLIZQVOyotd\nJKQIDOa2/wyzZ8/mwIED9aY99thjXHjhhW3ethL9SvMdJKZZut1RZ2fQaDWcd+1wzDEGNq46hLvK\ny9RfjkTbSHfr7qzbJXsIlkZOSDVTWuBst9LIqsSxcir2fAf9Vb/6DiOE4MzZgzDF6Pn+vb1Uu7xM\nvykTg6lbprhGddtdn0YbrJSpSiMrHc1VVY2r0qtq4HSCMVP78tPrR5C3q4wVC3NwVanyCjW6bbKH\nuqWRCZVGVpUylfZXmh8sf2FTyb5TjPhJGhfdNIqSvCqWPbmRSrs73CFFhG6d7KGmNLIFKaG80KlK\nIyvtzp7vAFBH9p1oQFYKM+/KwlHmYekTGyg97gh3SGHX7ZM9nCiNHGjH0siKUsOe70Bv1BJjU10u\nO1OvITYuu3csfl+ApU9spOBgRbhDCiuV7EP0Ri3xKQ1LI7e1nn1TXnjhBZYsWdLu21UiT2m+A1tP\n1RMnHFL6xDLnvnEYzFpWPL2JIztOfbd7V6aSfR0G84nSyBVFrnYrjdyYm2++meuvv77Dtq9EDnu+\nQzXhhFFCqoU5940jLtnEB89vZu+GwnCHFBbNSvZCiFeFEIVCiNw6034vhDgqhMgJPS6uM+9BIcRe\nIcQuIURUdTQ3WvTEJpmodvuoKHbVG/C4NfXs+/fvz/3339+gnn1rR6pSoovb4cVZXq0uzoaZNd7I\nZb8eS49+caz6Ry7bvjka7pA6XXM7oS4G/gac3O7wtJSyXsYSQowErgIygF7AZ0KIoVLKNvVtPP6X\nv+DZ0bJ69qdjHDGcnr/5TYPp5hgDMgBVpW6qSj1Ax9azV7qu0uPBnjiJvVSyDzeTVc+Mu7JZ9VIu\nq1/fhavKy7jp/bpN81qzjuyllF8DzW3smgX8R0rpkVIeAPYCE1sZX9hY4gxY4414nF4KCzu2nr3S\ndZWqnjgRRW/QctEtmQyd1IMfV+znu3f3dmhzbSRp6+1ltwshrgfWA/dKKUuBdOCHOsvkhaa1SWNH\n4B3NEm/AFGMgNiaWXmnpHVbPXum67PkOdHoNsYmmcIeihGi1Gi74xUhMVj2bPz+Cu8rLedcPR6vt\n2pcw2/LtFgGDgGwgH3gqNL2xbNborlMIMU8IsV4Isb6oqKgNoXQMIQTWeANGo5FX/v5vFr/2zw6p\nZ690XaX5DmxpVoRG7eQjidAIzr5iCJNmDmTXj8f57wtb8Xbxu+hbneyllAVSSr+UMgC8zImmmjyg\nT51FewPHmtjGS1LK8VLK8SkpKa0NpUMJIdBoBYlJ8fzzxTd56qkF7VbPXh3ld332fAe2NEu4w1Aa\nIYRg/MX9mXLNMA7llvD+szlduhJuq5txhBBpUsr80NvZQE1PnZXAG0KIBQQv0A4B1rYpyjDq378/\nubm5yIBEymT+u/QL4lPMzJo1iwULFjRrG7fddhsPP/xwvWklJSX069evI0JWIkS1y0dVqUe110e4\nUeekY7Lq+fTVbSx7ahMz7szCGt/1boBrbtfLN4E1wDAhRJ4Q4n+Ax4UQW4UQW4DzgHsApJTbgLeB\n7cDHwG1t7YkTCWpKI+sMWsqL3VS7fa3e1kMPPcSPP/7IzJkz2zFCJdLYQ7fo23qqZB/pBo9L5dLb\nsygvdrH0iQ2UFznDHVK761YjVbWHgD9AaYGTgE+S0MOC3qgFumY9+0j8/aPJju+P8cWSnfz8kTNI\n6KGacqJBwYEKPvjbZoRWMPPOLJJ7x4Y7pNNq7khVqthzC9WURi4rcFJW6MTWw4LOoFX17JUG7PlO\ntDoNccmqJ0606DEgjtnzx/L+szkse2oTl9w6ml5DEsIdVrvo2n2NOohWpyG+TmlkvyqNrDSiNN9B\nQg8Lmi7epa+rSUyzMue+cVjjDax8NocDW4rDHVK7UH+FraSrUxq5TJVGVhphDw1FqESf2EQTs+eP\nJamXlf++sJWdP+SffqUIp5J9GwRLI5sJ+CXlqjSyUofX46eyxK3KJEQxc4yBWfeMIX1oAp8v3kHO\nZ4fDHVKbqGTfRnqjjvgUMz5foF5pZKV7qxksQxVAi24Gk45Lb8ti0NgUvnt3L2uW7yNSOrW0lEr2\np9GcevbB0sjmRksjf/PNN2RkZJCdnc3Ro0e5/PLLWxzDxRdfTFlZWYvXU8JH1cTpOrR6DdP+dxQZ\nk3ux8eNDrH59V1Qe1Klk307qlUYuOVEa+fXXX2f+/Pnk5OSQnp7Ou+++22DdmrILTfnoo49ISOga\nPQK6C3u+E402eG+GEv00GsGUa4Yx/uL+bP/2GKtezsXnja7bh1Syb4HT1bM3xxiIsZnwOH1Ulrh5\n+eWXefvtt/nDH/7Az3/+83pnCYsXL+aKK65gxowZTJs2jdWrV3POOecwe/ZsRo4cyc0330wgELwG\n0L9/f4qLizl48CAjRozgxhtvJCMjg2nTpuFyuQDYt28f06dPZ9y4cUyePJmdO9u3HLTSMvZQT5yu\nXlyrOxFCMGnmQM6+Ygj7NxXxwd+2tOnmys4WNf3sv3l7N8VHqtp1m8l9Yph85dBmLdvcevaWOAMy\nIHGUe7hq7nV89913XHrppVx++eUcPHiw3jbXrFnDli1bSExMZPXq1axdu5bt27fTr18/pk+fztKl\nSxs0++zZs4c333yTl19+mSuvvJL33nuPa6+9lnnz5vHCCy8wZMgQfvzxR2699Va++OKLNv9GSuuU\n5jtI7hP5N+QoLZd1fh9MMXo+/+cOli/YxIw7sjDHGsId1mmpw45mKCpqWT17S7wBc6wBV2U1vlNU\n0ps6dWq9dSdOnMjAgQPRarVcffXVfPvttw3WGTBgANnZ2QCMGzeOgwcPUlVVxffff88VV1xBdnY2\nN910E/n50d9VLFr5qv2UF7tUt8subNiknlx8Syal+Q6WPrmRSrs73CGdVtQc2Tf3CLwjxMfH06dP\nn2bXsxdCEGMzIgMSX3UAj7PxUz2r1dpgvVO9BzAaTxRo0mq1uFwuAoEACQkJ5OTktORrKR2ktMAJ\nUvXE6er6ZyYz865sPvz7Ft57fAMz78yO6K626si+GQwGA8uXL2fJkiXNrmcvhCA2yYRGJ3A7qnFX\nnb506tq1azlw4ACBQIC33nqLs88+u1nxxcXFMWDAAN555x0ApJRs3ry5Bd9QaU+qJ073kTY4gct+\nPRYZkCx9agPHD5SHO6QmqWTfTFarlQ8++ICnn3662fXshRAYjDq0ei0VJS48rlMn/DPPPJMHHniA\nUaNGMWDAAGbPnt3s+F5//XVeeeUVsrKyyMjIYMWKFS36fkr7sec7EBpBQqpqxukOknvHMOe+cRgt\nelYszOHw9pJwh9QoVfWyEwQCkrICJz5vgIRUMwZTw9az1atX8+STT0bUYORd5ffvbP99YSv2fAc/\nf+SMcIeidCJHuYf3n9tMab6DC345kiHje3TK5za36qU6su8EGo0gIdWMVicoL3Th9URX/1ylZez5\njohuu1U6hjXeyOxfj6HHgDg+eWUbW1fnhTukelSybyezZ88mOzu73mPVqlW182tKIwutoKzQ2aCX\nzrnnnhtRR/VK6/i9AcqLXKq9vpsyWvTMvDOb/pnJfP2f3az78EDElFeImt44ka459ey1Og0JqeZQ\nLXwXth4WtHq1v+1KygqdyIBU4852YzqDlotuGsWX/9rJ2vcP4Kr0MvnKIWEfdD7iM02k7BXbi06v\nDZVGlhFdGrmr/e6dxa564igEz+R/ev0Isi/ow9bVeXz62vaw/1+P6GRvMpkoKSnpcomnfmlkV8SV\nRpZSUlJSgsmkRlhqqdJ8B0KgeuIoCI3gJ3MHc+bsQexZV8BHi7aE9XpdRDfj9O7dm7y8PIqKisId\nSofweQO4KqvRHhaY4wynvFGrs5lMJnr37h3uMKKOPd9JXHJwYHpFEUIw9sJ+mKx6Vr++k5XPbOKS\n27IwWfWdHktEJ3u9Xs+AAQPCHUaH2repkFUv5dJ7RCKX3DJateFHOXu+Q905qzQw8uxeGK06Pnll\nG8ue2siMO7KJsRlPv2I7UpklzAaNSeW864ZzZLudT1/bFpV1spUgvz9AeYFTtdcrjRo0JpUZt2dR\nWeJm6RMbKCtwdurnq2QfAUb8pBdnXT6YfRuLWP36zi53jaK7KA+NVKYKoClN6T08kct+PQZvtZ+l\nT26g6HBlp322SvYRIvuCvoy/uD87vsvn+6XRO/RZd1ZTE0c14yinktovjrn3jUOr17BswUaO7irt\nlM9VyT6CTJwxgMxze5Pz6WE2fHwo3OEoLVTT7dLWUyV75dQSeliYe984Ymwm3n9uM/tzOr4Tikr2\nEUQIweQrhzB0Ug9+XLGf3K8i63Zr5dRK8x3EJZvQG1VPHOX0Ymwm5tw7luQ+MXz84lZ2fH+sQz9P\nJfsIIzSERglqAAAgAElEQVSCn14/gv6jk/nqP7vZvfZ4uENSmsme71RNOEqLmGL0zLp7DH1GJPLF\nkp1s/KTjzuhVso9AWq2GC2/MIH1IAp8t3sHBLcXhDkk5jYA/QFmBk0TVhKO0kN6o5eJbRzNkfCpr\nlu7j+/f2dsg1O5XsI5ROH/wDSOkTw8cv53J0d+dcxFFap6LYjd8XUEf2SqtodRqm/iqDzCnpbPr0\nMF/8a2e731mvkn0EM5h0XHpHFnFJJj78+xYKD1WEOySlCaomjtJWQiOYfNVQJlzSn53f5/PxS7n4\nvO1XXkEl+whnjjEw865sTBZ9cGCE445wh6Q0oubfRVW7VNpCCMHEGQOZ/LOhHNhSzPvPbsbjanwM\n65ZSyT4KxNhMzLw7G6ERrHwmh4oSV7hDUk5iz3cQYzM2OgqZorTU6PN6M/VXIzm+r5zlCzbirKhu\n8zabneyFEK8KIQqFELl1piUKIT4VQuwJPdtC04UQ4lkhxF4hxBYhxNg2R9rNJaRamHlnNl6Pn5XP\n5LTLP77SfuzHHKoJR2lXQyf05OLbRlNW4GTpExuoKG7bQV5LjuwXA9NPmvYA8LmUcgjweeg9wEXA\nkNBjHrCoTVEqQHBg40tuy8JR5mHlszl4nKcewFzpHIGApPS46naptL9+GUnMunsMboeX957YQMnR\nqlZvq9nJXkr5NWA/afIs4J+h1/8ELqszfYkM+gFIEEKktTpKpVbaoHguuimT0nwHHz6/BW+1Gs82\n3CpL3Pi9AXVkr3SIngPjmT1/LAJY9tRG8veVt2o7bW2z7yGlzAcIPaeGpqcDR+oslxeaprSDvhlJ\nTP1VBsf3l/Pxi1vDPgJOd6dq4igdLalXDHPuG4cpRs/KhZs4lFvS4m101AXaxkbhaHCXgBBinhBi\nvRBifVcdoKSjDB6XyrnXDufwNjufvrpdlUYOoxPdLlVPHKXjxCWbmTN/HLY0Kx/9fUuL765va7Iv\nqGmeCT0XhqbnAX3qLNcbaFD4QUr5kpRyvJRyfEpKShtD6X5GntWLn8wdzL6NhXz1xi5VKTNMSvMd\nWOMNGC2dP/qQ0r1Y4gxcds8Y0gbH8+mr29ny5ZHTrxTS1mS/EvhF6PUvgBV1pl8f6pVzBlBe09yj\ntK8xU/sy7qJ+bP/2GGuW7Qt3ON2SGp1K6UwGc/BmywFZyXzz1p5mr9eSrpdvAmuAYUKIPCHE/wCP\nAlOFEHuAqaH3AB8B+4G9wMvArc2OSGmxSTMHMmpKOps+OcyGjw+GO5xuRUqJ/bganUrpXDq9lunz\nRjHirOb3e2n2HSBSyqubmHV+I8tK4LZmR6G0iRCCc342FI/Txw/L92O06Bl1jroe3hmqSj34PH51\nZK90Oo1Ww3nXDofrm7e8ut2vixAawfk3jMDr9vHVm7swmLUMndAz3GF1eaomjhJOQjTWF6ZxqlxC\nFxIsjTyKXoMT+Py1HRzcqkojd7RSleyVKKGSfRejM2i55NbRJPWO4eOXcjm2pyzcIXVp9mMOzLF6\nTDGqJ44S2VSy74IMZh0zakojP7+5U0ew727s+aomjhIdVLLvosyxBmbcmY3BouP953JUaeQOIKWk\nVHW7VKKESvZdWGyiiVl3jQFg5TM5VNrdYY6oa3GUVVPt9qsjeyUqqGTfxSX0sDDzrmyq3ao0cntT\nF2eVaKKSfTeQ3DuWS28bTZXdzfvP5bTbyDfdnV0VQFOiiEr23UTa4ASm35yJ/ZiDD5/frEojtwP7\ncQcmqx5zrOqJo0Q+ley7kX4ZSVzwy5Hk7yvn4xdzVWnkNgpenLW06MYWRQkXley7mSHje3DuNcM4\nvK2Ezxar0sitJaVUQxEqUUWVS+iGMian43H5WLN0H0azjinXDFNHpy3kqvTicfpUe70SNVSy76bG\nTuuHx+lj48eHMFr0nDl7ULhDiiqqJo4SbVSy78bOmDUwmPBXHcJo0TH2wn7hDilq2I+pZK9EF5Xs\nuzEhBOdcNZRqp5c1y/ZhtOjImKxKIzdHab4Dg1mHJd4Q7lAUpVlUsu/mNBrB+b8cSbXbz+o3dmEw\n6xgyvke4w4p4wZo4qieOEj1UbxwlWBp53ijSBsXz2avbObSt5SPXdzelx1VNHCW6qGSvAKA3aLnk\ntqxgaeQXtnJsryqN3BRXZTWuSq9qr1eiikr2Si1jqDRyTKKJD5/fQtERVRq5MTUVRFWyV6KJSvZK\nPeZYAzPvysZg0vL+szmUFTjDHVLEsecHfxPVjKNEE5XslQZiE03MujtYGnnFwk2qNPJJ7PkO9EYt\nMTZjuENRlGZTyV5pVEIPCzPuyKba5WPlMzm4KlVp5Bo1A5aonjhKNFHJXmlSSt9YLrk9K1QaebMq\njRxS0+1SUaKJSvbKKfUanMD0mzIpyavio79vwdfNSyO7HV6c5dWqvV6JOirZK6fVb1QSF/xqJMf2\nlvHxS7n4vN034ZceD16cVT1xlGijkr3SLDWlkQ/llrDsyY1UlXbPi7b2Y1WASvZK9FHJXmm2jMnp\nXHRzJqXHnbz9l3Xd8sar0nwnOr2G2ERTuENRlBZRyV5pkYHZKVx+/3gMZh0rFmwi96s8pOw+A6DY\nQ2UShEb1xFGii0r2Sosl9rJyxQPj6TMyka/e3M3qf+/E7+0eQxyW5qvRqZTopJK90ipGi56Lbx3N\nuIv6sf27fJYt2IijzBPusDpUtctHVakHm+p2qUQhleyVVtNoBGfMGsT0eaMoOebg7b+s4/j+8nCH\n1WHsqiaOEsVUslfabNDYVC7/f+PQGTQse2oj2745Gu6QOkRpaChC1cdeiUYq2SvtIik9hisenED6\nMBurX9/F6td34vd1rXZ8e74TrU5DXLI53KEoSou1S7IXQhwUQmwVQuQIIdaHpiUKIT4VQuwJPdva\n47OUyGWy6rn09izGXtiXbd8cY8XTm3CUd512/NJ8Bwk9LWhUTxwlCrXnkf15UspsKeX40PsHgM+l\nlEOAz0PvlS5OoxGcOXsw0/43g6Ijlbzz1/UUHKgId1jtwq564ihRrCObcWYB/wy9/idwWQd+lhJh\nhozvwdz/Nw6NVrD0qQ3s+P5YuENqE6/HT2WJWxVAU6JWeyV7CXwihNgghJgXmtZDSpkPEHpOPXkl\nIcQ8IcR6IcT6oqKidgpFiRTJvWO58sEJ9BqcwBdLdvL1m7vw+6OzHb9mdCp1cVaJVu2V7M+SUo4F\nLgJuE0Kc05yVpJQvSSnHSynHp6SktFMoSiQxxeiZcUcW2Rf0YetXR1m5MAdnRfTVxrfnq26XSnRr\nl2QvpTwWei4ElgETgQIhRBpA6LmwPT5LiT4arYazLh/C1F+NpOBgBe/8dR2Fh6KrHb8034FGK4hL\nUT1xlOika+sGhBBWQCOlrAy9ngb8AVgJ/AJ4NPS8oq2fpUS3oRN7Yutp5aMXtrD0iY2ce+0whp+R\nFu6wmsWe7yShhwWttnXHRwG3m+Lnn0dWexEWMxqLBY3ZgsZsRhN6L8zm4DSLOTTdgrBYEHq9GhVL\nabM2J3ugB7As9MeoA96QUn4shFgHvC2E+B/gMHBFO3yWEuVS+gbb8Ve9nMvni3dQdLiSn8wd3Ook\n2lns+Q5S+8a2fv3XXqPk5X8gLBakywUtKR6n1QaTf50dQN33Gos5uKOwWIm7cBrmrKxWx6l0XW1O\n9lLK/UCDvy4pZQlwflu3r3Q95lgDM+7K5vv39rLlizxKjlZx4Y2jMMcYwh1ao3zVfiqKXQyb1LN1\n6xcVUfzyP4idOpXezz2LlBLpdhNwuQg4XQScDqTLdeK9y0nA6QxOc4am10yrfe/CX1WJr7DgxDKV\nlZT+5z/0W7IE86iMdv4VlGjXHkf2itJiWq2GyVcOJaVvLKv/vYt3/rKei27OJKUNR88dpbTACbL1\nF2eL/vY8srqa1Ht/DYAQItRkY4bE9ovTW1jIwauu4sgtN9P/zf9g6J3efhtXol5knzsrXd7wM9KY\nc99YpJQsfWIDu9cdD3dIDZyoidPyPvaevXspe+cdbFdfjaF//3aOrD59aip9X3oJ6fZw5Kab8Jd3\n3aJ0SsupZK+EXWq/OK54cAIp/WL59JXtfPfuHgIR1B/fnu9AaAQJqS1P9oVPPInGaiX51ls6ILKG\njIMH0/tvf6P68GHy7riTQHX0dXNVOoZK9kpEsMQZmHX3GDKnpJPz2RHef24z7ipvuMMCgkMRJqSa\n0epa9t/FsWYNVV99RfLNN6GzdV5pKOukifT6y19wrl1L/m9+261GElOappK9EjG0Og3nXD2M864b\nzrG9Zbzz6DqK86rCHRb2fEeL75yVgQAFjz+BvlcvbNde20GRNS1+xqWk3HMPFR98QNHCZzr985XI\no5K9EnFGntWL2feOxe8N8N7j69mzviBssfi9AcqLXC2+OFu+ciWeHTtIueceNEZjB0V3aknzbiTh\nyispefFFSt96OywxKJFDJXslIvUcEM8Vv5lAcu9YPvnHNtYs24vfF+j0JomyQicyIFt0cTbgdlO0\n8BlMo0YRd8nFHRjdqQkh6Pl/D2E9ZzLH//AHqr76KmyxKOGnul4qEcsab+SyX4/hm7d2s3HVYTau\nOgwEyygLrUCjFWg0dZ81CK1AqxUIzUnztRqEJjTvpOk1r4VWoK3ddnB6RYkLaFm3S/s/l+A7fpxe\njz+G0IT3eErodKQveJpD119H3j2/pt+/lmDOUH3wuyOV7JWIptVpOPfnw+mbkUTJ0SoCAUnAL5H+\n4HMgEHr4A0i/xO+XyECdef5A8LVfEvAF8Hpq5gfqLBOc5q/dbqB2esAvibEZsfVoXrL3lZRQ8tJL\nxJx/PtaJEzv412kebYyVPote4ODVV3Hk5psZ8J//oE9XffC7G5XslagwMDuFgdnhqYwqpWx2bZri\n558n4HaTeu+9HRxVy+h7pNL3xRc5eM3POXzTTfR/4w20cXHhDkvpRKrNXlFOo7mJ3rN/P6VvvY3t\nZz/DOHBAB0fVcsYhQ+j93HNUHzpM3u13qD743YxK9orSTgqffAqNyUTy7beFO5QmWc+YRK+//DnY\nB/+3v1N98LsR1YyjKO3A8eNaqr74gpR77kGX2I4FbzpA/IwZeI8eo2jhQvTpvUi9++5wh6R0ApXs\nFaWNZCBA4eOPo+vZk8RfXB/ucJol6aZ5eI8epeSFF4M3fl15ZbhDUjpYxCR7b34+xS+9jL5HKroe\nPdCl9kDfIxWNVQ0Dp0S2ig8/xL1tG70eexSNyRTucJpFCEHPh/8P7/HjHH/kD+h79iTmnGaNJqpE\nKREpbXajrFb5Tp++DaZrYmLQ9Qgmfl1qj+COoEcq+tAOQdcjFV1SEkKrDUPUSncX8HjYd9FFaBMS\nGPDuu2HvV99S/ioHh66/juqDh1Qf/CglhNggpRx/2uUiJdmPHz9erv3qK7yFhfgKCvEVFuAtKAi+\nLijAWxh6XVQEfn/9lbVadMnJDXYK6ixB6Wgl//gHhU8+Rd/Fr2E944ywxbHhkJ3fLM0lOdbAny/L\npH9y8//WvQXBOvjS51V98KNQVCb79evXn3Y56ffjKympv0Oo2UHU2SkEKisbrHvqs4TQtGR1lqA0\nj6+0lH1Tp2EZP54+LywKSwzOah9PrtrNa98fIC3ORKXHR7UvwL3ThvI/Zw9Eq2lmt9E9ezh4zc/R\n9UhVffCjTJdN9s0VcDpPnBmoswSlAxz/058pffNNBq5cgXHQoE7//O/3FvPA0q0ctju5/sx+/L/p\nw6ly+/jd8lw+21FAVu94Hr88i2E9mzf6l+OHHzh84zwsY8fS9+WXEIbIHCZSqa/bJ/vmkH4/frsd\nb2iH4CtofKdwqrMEXWoK+pPPEmquJ6izhC7Lc+AA+2fMJGHuXNIe+f1pl193fB1/z/k7Dq+jzZ8d\nkJLj5W7sDi8GnSDdZsFqCP6dpVpSGZ44HGdlGv/5NkClw8Kt5w7htvMGY2hGPf7ylSs59v/uJ27m\nDHo99lizbyhTwqe5yT5ieuOEg9Bq0aWkoEtJAZq+MHXyWYKvsDC4gygI7iAca9cGzxJ8vvorNuMs\nQZeaijZGnSVEIme1jzfXHmFPQSU3TxlUrx28aMECNAYDKae5gcrpdbJw40Le3Pkm6THpDE4Y3KaY\niqs87D5eicdnom9iIgOTY2qbaiSSo1VH+eboNwRkANIhljhe2t2Tt/cN5FcTzuaSYRNIs6Y1mcTj\nZ87Ee+wYRQufQZ+eTupdd7UpXiVydOtk31waiwXjgAEYBzR9C7wMBPCXlDR5luA5cADHDz82fpZg\ntZ44M1BnCWFX5qxm8fcHWfz9QcqcXgxaDcs2HeXuC4byv5MH4N20kcpPPyPlrjtDBwqNW398PQ99\n9xBHq45y7YhruXPsnZh15lbH9If3t/PDpqMM7RHD45dnkd0nodFlnV4nu0t3s71kOzvsO1h7dAvH\nnJ/wbO7HPJsLcYZ4RiaNYGTSSEYkjSAjMYPesb1rdwBJN90U7IO/6IVgH/wrrmhVzErbSSkJVFXh\nt9vx2e34S8vwl4Ze20vxl5Y2e1vduhknHAJO54kzg9qdwomzBG9hYYvOEnSpKfV2CuosofWOl7v5\nxzf7eWPtYZzVfi4Ykcot5w6it83Cwyu28fG242T0jOGJL59FX1rMoFUfozE3TN5Or5NnNz3L6zte\np09sH/541h8Z12Ncq+P679Z8HlqxjTJnNbeeN5jbzhuEUdeyHX+Ro4r/+++nfLZ3I/EJBfRILuGY\naz++QPDvLFYfy4ikEYxIDO4EhscPQffA4zi+X0OfFxYRM3lyq+NXTpB+P/6yslDyDibrk5O3rzT0\n2m7HV1YG3saH5xQmE9pEG0O//FK12UerRs8S6vQ4Cl5wLiRQUdFg3a56liADARCiQ9qQ9xdV8eJX\n+1m6KY+AhJlZvbhpykCG96zfI+Xj3Hw+XLiEW79dzIarbmfub27GbKj/O24o2MBD3z3EkcojXDP8\nGu4aexcWfcsHKgcoqvTw8MpcPtp6nIxecTxxeRYje7Wtl8z3+4p5cOlWDpU4uXJCGlecqeNw1Z7a\ns4Bd9l1UB4IF0hL9Zh553U9SSTXHHr+NQRMuYED8ALSa6Prb6UgBj6fBUXfDRB5M3H67HX9FBTSR\nczVxcehsNrQ2G9rERLSJNnS2xNB7G7rERLSh97pEGxpL8O9KXaDtBlp9lqDR1J4lnGqn0NazBOn3\nE3A6CTgcBKqqgs8OB/6qKgIOZ51pdec56i9f8+x0ok1Jxpw5GvPoTEyjMjFnjkIbH9/q+LbmlbPo\nq738N/c4Bq2GK8f3Yd45A+mT2HhyDlRXs3f6RRRJPddNvI30JCt/mZ3J5CEpuHwunt0YPJpPj0nn\nD2f9gQk9J7Tud5OS5TlHeeT97Tg9fu66YAjzzhmIXts+N2y5qv0s+HQXr3x7gNRYE3+ZM4qfDu8B\ngDfgZX/Z/trkf3h/Dtc+nYsISH57vRZnkoWhtqGMTBpJekw6NpONBGNC7SPeGE+sIRaNiK6by6B+\nk4m/tDSUsJs+6vaXlhJwOhvfmFaLNiEBXaItmKATE4OvE2wnXieGErnNhs5mQ+j1rYpbJXsFCJ0l\n2O1NdkNt6VmCNtGGdLvrJG0HgZMTtMOB3+FANvUf4WR6PVqrFY3ViiYmJvhstaKJCT5rrTForBa8\nR4/i2rKV6gMHalc19OuHKTMzuAPIzMQ0YsQpSxZIKVmzv4RFq/fxzZ5iYo06rjuzH788awApsace\nK7bk1dcofPxx+r76Crk9h/Hg0q3sL3bw0ywn+YbF5FUd5qphV3HPuHtafTSfX+7it8ty+WJnIWP7\nJvD45aMZnNq8rpMtlXOkjPvf3cKugkpmZffi4RkZJFobdrd07trJoWt+jic5ls8f+Clb3PvYad+J\n09f4v69WaIk3xtfbAdhMtuCz0dZgWoIxgXhDfLufMdRrMiktDSXsOkfhdvuJ5F0afMimmkyMxmCS\nrnfUXZPIa466g+91iTY0cXGddjd11CX7uEFxcvrC6SSaEkkwJWAz2rCZQg9j/ec4Q5w6lWxnAZfr\nxJlBI01H3sICfIUnzhKEwVAnKcfUJufapG2NqTPPEkzaMTEnLR96bmF/bn9FBe5t23Bt2Yo7dyuu\nLVvxFYQGJdfpMA4dEjwDyByFKXM0xsGDkELDpzsKWLR6HzlHykiOMfCrswdw7Rn9iDOd/ojKV1rK\nvgunY87Kou/LLwFQ5nJw0wd/ZpvjA4Tfxv8Mu5+7zrqoVU1NUkreWneEP3+4A28gwH0XDueGn/Rv\n9k1RrVXtC/D8l3v5++q9xJr0/H5mBjNGN+ytc3IffPR6Kr2VlLnLKPPUeZz8PvQod5dT6inFG2gi\nmSKINcTW2wHUe5gSSBBWbC4NcU6wOvyYq7xQVn5SIj9x1O0vL2+6ySQ29kQzSWIiWlvCiWaSmkQe\neq9LtCHM5ojthhp1yT5teJqc/bfZlHnKsLvtlHnKmuyTrBEa4g3xTe4UEowJtTuNRGPwubW9IJQT\nZCBAoKoKjckUcTfceAsKaxO/e+tWXLm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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe17eb29be0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"flv_oert = pd.read_csv('/home/greg/data/rawdata/MojaveCarbon/modeling_background/flv_soilCO2flux.csv',\n",
" skiprows=1,index_col=0, parse_dates=True)\n",
"kc_amund = pd.read_csv('/home/greg/data/rawdata/MojaveCarbon/modeling_background/kylecyn_soilCO2flux.csv',\n",
" skiprows=1,index_col=0, parse_dates=True)\n",
"# Convert\n",
"flv_oert_c = (flv_oert*1000)/(10000*12)\n",
"kc_amund_c = (kc_amund*1000)/(10000*12)\n",
"f1 = flv_oert_c.plot()\n",
"f2 = kc_amund_c.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Parse MNP daycent data"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parsing /home/greg/current/mojaveCENT/DAYCENT_mnp/mnpjtree.out/mnpjtree_Eq_master.lis\n",
"Parsing /home/greg/current/mojaveCENT/DAYCENT_mnp/mnppj.out/mnppj_Eq_master.lis\n"
]
}
],
"source": [
"path = '/home/greg/current/mojaveCENT/DAYCENT_mnp/'\n",
"#mnp_creo = ld.load_century_lis(path + 'mnpcreosote.out/mnpcreosote_Eq_master.lis').loc[5997:5999]\n",
"mnp_jtree = ld.load_century_lis(path + 'mnpjtree.out/mnpjtree_Eq_master.lis').loc[5997:5999]\n",
"mnp_pj = ld.load_century_lis(path + 'mnppj.out/mnppj_Eq_master.lis').loc[5997:5999]"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>pet</th>\n",
" <th>evap</th>\n",
" <th>tran</th>\n",
" <th>stemp</th>\n",
" <th>tave</th>\n",
" <th>agdefac</th>\n",
" <th>bgdefac</th>\n",
" <th>fcacc</th>\n",
" <th>rleavc</th>\n",
" <th>fbrchc</th>\n",
" <th>...</th>\n",
" <th>metabc(2)</th>\n",
" <th>strucc(1)</th>\n",
" <th>strucc(2)</th>\n",
" <th>agcacc</th>\n",
" <th>bgcjacc</th>\n",
" <th>bgcjprd</th>\n",
" <th>tnetmn(1)</th>\n",
" <th>tminrl(1)</th>\n",
" <th>somte(1)</th>\n",
" <th>resp(1)</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5997.00</th>\n",
" <td>3.1469</td>\n",
" <td>0.9800</td>\n",
" <td>0.0145</td>\n",
" <td>0.7850</td>\n",
" <td>5.0750</td>\n",
" <td>0.0186</td>\n",
" <td>0.1076</td>\n",
" <td>0.0000</td>\n",
" <td>94.2521</td>\n",
" <td>338.9945</td>\n",
" <td>...</td>\n",
" <td>19.2378</td>\n",
" <td>2720.2627</td>\n",
" <td>218.1249</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>9.4666</td>\n",
" <td>0.4690</td>\n",
" <td>309.9537</td>\n",
" <td>391.1839</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5997.08</th>\n",
" <td>3.2642</td>\n",
" <td>1.0182</td>\n",
" <td>0.0156</td>\n",
" <td>-1.2118</td>\n",
" <td>3.0133</td>\n",
" <td>0.0091</td>\n",
" <td>0.0769</td>\n",
" <td>0.0000</td>\n",
" <td>91.4652</td>\n",
" <td>325.6938</td>\n",
" <td>...</td>\n",
" <td>23.8913</td>\n",
" <td>2720.9231</td>\n",
" <td>220.4455</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.2594</td>\n",
" <td>0.5145</td>\n",
" <td>310.2385</td>\n",
" <td>9.9703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5997.17</th>\n",
" <td>4.3940</td>\n",
" <td>0.8213</td>\n",
" <td>0.0215</td>\n",
" <td>1.8047</td>\n",
" <td>6.2567</td>\n",
" <td>0.0051</td>\n",
" <td>0.0784</td>\n",
" <td>0.0000</td>\n",
" <td>88.7606</td>\n",
" <td>312.9143</td>\n",
" <td>...</td>\n",
" <td>27.2013</td>\n",
" <td>2722.1633</td>\n",
" <td>222.3897</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.5493</td>\n",
" <td>0.6280</td>\n",
" <td>310.4135</td>\n",
" <td>18.3334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5997.25</th>\n",
" <td>7.9160</td>\n",
" <td>0.9793</td>\n",
" <td>0.0391</td>\n",
" <td>5.5021</td>\n",
" <td>10.9400</td>\n",
" <td>0.0067</td>\n",
" <td>0.1019</td>\n",
" <td>20.6972</td>\n",
" <td>104.2564</td>\n",
" <td>300.6371</td>\n",
" <td>...</td>\n",
" <td>28.8105</td>\n",
" <td>2723.2834</td>\n",
" <td>223.6520</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.9393</td>\n",
" <td>0.7823</td>\n",
" <td>310.5346</td>\n",
" <td>29.4845</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5997.33</th>\n",
" <td>9.9627</td>\n",
" <td>0.8765</td>\n",
" <td>0.0498</td>\n",
" <td>7.0826</td>\n",
" <td>12.3200</td>\n",
" <td>0.0074</td>\n",
" <td>0.1131</td>\n",
" <td>68.7262</td>\n",
" <td>142.6594</td>\n",
" <td>288.8412</td>\n",
" <td>...</td>\n",
" <td>28.9214</td>\n",
" <td>2724.9246</td>\n",
" <td>225.2185</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.4136</td>\n",
" <td>0.1878</td>\n",
" <td>310.4187</td>\n",
" <td>41.9601</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 33 columns</p>\n",
"</div>"
],
"text/plain": [
" pet evap tran stemp tave agdefac bgdefac fcacc \\\n",
"Date \n",
"5997.00 3.1469 0.9800 0.0145 0.7850 5.0750 0.0186 0.1076 0.0000 \n",
"5997.08 3.2642 1.0182 0.0156 -1.2118 3.0133 0.0091 0.0769 0.0000 \n",
"5997.17 4.3940 0.8213 0.0215 1.8047 6.2567 0.0051 0.0784 0.0000 \n",
"5997.25 7.9160 0.9793 0.0391 5.5021 10.9400 0.0067 0.1019 20.6972 \n",
"5997.33 9.9627 0.8765 0.0498 7.0826 12.3200 0.0074 0.1131 68.7262 \n",
"\n",
" rleavc fbrchc ... metabc(2) strucc(1) strucc(2) \\\n",
"Date ... \n",
"5997.00 94.2521 338.9945 ... 19.2378 2720.2627 218.1249 \n",
"5997.08 91.4652 325.6938 ... 23.8913 2720.9231 220.4455 \n",
"5997.17 88.7606 312.9143 ... 27.2013 2722.1633 222.3897 \n",
"5997.25 104.2564 300.6371 ... 28.8105 2723.2834 223.6520 \n",
"5997.33 142.6594 288.8412 ... 28.9214 2724.9246 225.2185 \n",
"\n",
" agcacc bgcjacc bgcjprd tnetmn(1) tminrl(1) somte(1) resp(1) \n",
"Date \n",
"5997.00 0.0 0.0 0.0 9.4666 0.4690 309.9537 391.1839 \n",
"5997.08 0.0 0.0 0.0 0.2594 0.5145 310.2385 9.9703 \n",
"5997.17 0.0 0.0 0.0 0.5493 0.6280 310.4135 18.3334 \n",
"5997.25 0.0 0.0 0.0 0.9393 0.7823 310.5346 29.4845 \n",
"5997.33 0.0 0.0 0.0 1.4136 0.1878 310.4187 41.9601 \n",
"\n",
"[5 rows x 33 columns]"
]
},
"execution_count": 179,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mnp_pj.head()"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DatetimeIndex(['1985-12-31', '1986-01-31', '1986-02-28', '1986-03-31',\n",
" '1986-04-30', '1986-05-31', '1986-06-30', '1986-07-31',\n",
" '1986-08-31', '1986-09-30', '1986-10-31', '1986-11-30',\n",
" '1986-12-31', '1987-01-31', '1987-02-28', '1987-03-31',\n",
" '1987-04-30', '1987-05-31', '1987-06-30', '1987-07-31',\n",
" '1987-08-31', '1987-09-30', '1987-10-31', '1987-11-30',\n",
" '1987-12-31'],\n",
" dtype='datetime64[ns]', freq='M')\n"
]
}
],
"source": [
"# Reindex century data with datetime objects\n",
"newix = pd.date_range('1985-12-31', '1987-12-31', freq='M')\n",
"print(newix)\n",
"\n",
"#mnp_creo.index = cent_idx\n",
"mnp_jtree.index = newix\n",
"mnp_pj.index = newix"
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Function to convert the Century accumulator to monthly values\n",
"def accum_to_monthly(df, varstr):\n",
" jan = df.loc[df.index.month==1, varstr]\n",
" df2 = df[varstr].diff()\n",
" df2[df.index.month==1] = jan\n",
" return df2"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1985-12-31 NaN\n",
"1986-01-31 16.8426\n",
"1986-02-28 20.8375\n",
"1986-03-31 28.0172\n",
"1986-04-30 32.9256\n",
"Freq: M, Name: resp(1), dtype: float64"
]
},
"execution_count": 182,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get monthly soil resp values from the accumulator\n",
"#creo_kc_mon = accum_to_monthly(mnp_creo, 'resp(1)')\n",
"jtree_kc_mon = accum_to_monthly(mnp_jtree, 'resp(1)')\n",
"pj_kc_mon = accum_to_monthly(mnp_pj, 'resp(1)')\n",
"jtree_kc_mon.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pars Century data for Fish Lake Valley"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parsing /home/greg/current/mojaveCENT/DAYCENT_flv/flvsaltbush.out/flvsaltbush_Eq_master.lis\n",
"Parsing /home/greg/current/mojaveCENT/DAYCENT_flv/flvblackbrush.out/flvblackbrush_Eq_master.lis\n",
"Parsing /home/greg/current/mojaveCENT/DAYCENT_flv/flvsagebrush.out/flvsagebrush_Eq_master.lis\n",
"Parsing /home/greg/current/mojaveCENT/DAYCENT_flv/flvpinyon.out/flvpinyon_Eq_master.lis\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>pet</th>\n",
" <th>evap</th>\n",
" <th>tran</th>\n",
" <th>stemp</th>\n",
" <th>tave</th>\n",
" <th>agdefac</th>\n",
" <th>bgdefac</th>\n",
" <th>fcacc</th>\n",
" <th>rleavc</th>\n",
" <th>fbrchc</th>\n",
" <th>...</th>\n",
" <th>metabc(2)</th>\n",
" <th>strucc(1)</th>\n",
" <th>strucc(2)</th>\n",
" <th>agcacc</th>\n",
" <th>bgcjacc</th>\n",
" <th>bgcjprd</th>\n",
" <th>tnetmn(1)</th>\n",
" <th>tminrl(1)</th>\n",
" <th>somte(1)</th>\n",
" <th>resp(1)</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5999.58</th>\n",
" <td>14.2915</td>\n",
" <td>1.0932</td>\n",
" <td>0.0714</td>\n",
" <td>10.4616</td>\n",
" <td>17.1400</td>\n",
" <td>0.0089</td>\n",
" <td>0.1357</td>\n",
" <td>26.4714</td>\n",
" <td>27.6168</td>\n",
" <td>8.8112</td>\n",
" <td>...</td>\n",
" <td>1.0083</td>\n",
" <td>406.7794</td>\n",
" <td>14.0659</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.2033</td>\n",
" <td>0.1163</td>\n",
" <td>37.9500</td>\n",
" <td>9.2877</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5999.67</th>\n",
" <td>11.8080</td>\n",
" <td>0.9782</td>\n",
" <td>0.0590</td>\n",
" <td>8.2062</td>\n",
" <td>14.6767</td>\n",
" <td>0.0087</td>\n",
" <td>0.1338</td>\n",
" <td>30.9592</td>\n",
" <td>30.7117</td>\n",
" <td>8.4655</td>\n",
" <td>...</td>\n",
" <td>0.9274</td>\n",
" <td>406.9257</td>\n",
" <td>14.0478</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.2499</td>\n",
" <td>0.1144</td>\n",
" <td>37.9242</td>\n",
" <td>10.4417</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5999.75</th>\n",
" <td>9.1499</td>\n",
" <td>0.8365</td>\n",
" <td>0.0457</td>\n",
" <td>7.5209</td>\n",
" <td>14.1367</td>\n",
" <td>0.0084</td>\n",
" <td>0.1289</td>\n",
" <td>34.9615</td>\n",
" <td>33.3255</td>\n",
" <td>8.1333</td>\n",
" <td>...</td>\n",
" <td>0.8687</td>\n",
" <td>407.1227</td>\n",
" <td>14.0331</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.2938</td>\n",
" <td>0.1143</td>\n",
" <td>37.9007</td>\n",
" <td>11.5459</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5999.83</th>\n",
" <td>4.6416</td>\n",
" <td>1.3968</td>\n",
" <td>0.0196</td>\n",
" <td>-1.4846</td>\n",
" <td>4.4633</td>\n",
" <td>0.0130</td>\n",
" <td>0.0744</td>\n",
" <td>35.5652</td>\n",
" <td>27.1622</td>\n",
" <td>7.8142</td>\n",
" <td>...</td>\n",
" <td>0.9215</td>\n",
" <td>410.1960</td>\n",
" <td>14.0531</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.3213</td>\n",
" <td>0.1090</td>\n",
" <td>37.9024</td>\n",
" <td>12.6517</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5999.92</th>\n",
" <td>3.0184</td>\n",
" <td>1.0923</td>\n",
" <td>0.0141</td>\n",
" <td>-2.6477</td>\n",
" <td>2.5617</td>\n",
" <td>0.0103</td>\n",
" <td>0.0929</td>\n",
" <td>35.5652</td>\n",
" <td>17.2402</td>\n",
" <td>7.5076</td>\n",
" <td>...</td>\n",
" <td>0.9667</td>\n",
" <td>414.9592</td>\n",
" <td>14.0729</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.3539</td>\n",
" <td>0.1603</td>\n",
" <td>37.8981</td>\n",
" <td>13.7188</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 33 columns</p>\n",
"</div>"
],
"text/plain": [
" pet evap tran stemp tave agdefac bgdefac fcacc \\\n",
"Date \n",
"5999.58 14.2915 1.0932 0.0714 10.4616 17.1400 0.0089 0.1357 26.4714 \n",
"5999.67 11.8080 0.9782 0.0590 8.2062 14.6767 0.0087 0.1338 30.9592 \n",
"5999.75 9.1499 0.8365 0.0457 7.5209 14.1367 0.0084 0.1289 34.9615 \n",
"5999.83 4.6416 1.3968 0.0196 -1.4846 4.4633 0.0130 0.0744 35.5652 \n",
"5999.92 3.0184 1.0923 0.0141 -2.6477 2.5617 0.0103 0.0929 35.5652 \n",
"\n",
" rleavc fbrchc ... metabc(2) strucc(1) strucc(2) agcacc \\\n",
"Date ... \n",
"5999.58 27.6168 8.8112 ... 1.0083 406.7794 14.0659 0.0 \n",
"5999.67 30.7117 8.4655 ... 0.9274 406.9257 14.0478 0.0 \n",
"5999.75 33.3255 8.1333 ... 0.8687 407.1227 14.0331 0.0 \n",
"5999.83 27.1622 7.8142 ... 0.9215 410.1960 14.0531 0.0 \n",
"5999.92 17.2402 7.5076 ... 0.9667 414.9592 14.0729 0.0 \n",
"\n",
" bgcjacc bgcjprd tnetmn(1) tminrl(1) somte(1) resp(1) \n",
"Date \n",
"5999.58 0.0 0.0 0.2033 0.1163 37.9500 9.2877 \n",
"5999.67 0.0 0.0 0.2499 0.1144 37.9242 10.4417 \n",
"5999.75 0.0 0.0 0.2938 0.1143 37.9007 11.5459 \n",
"5999.83 0.0 0.0 0.3213 0.1090 37.9024 12.6517 \n",
"5999.92 0.0 0.0 0.3539 0.1603 37.8981 13.7188 \n",
"\n",
"[5 rows x 33 columns]"
]
},
"execution_count": 183,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path = '/home/greg/current/mojaveCENT/DAYCENT_flv/'\n",
"flv_salt = ld.load_century_lis(path + 'flvsaltbush.out/flvsaltbush_Eq_master.lis').loc[5997:5999.92]\n",
"flv_black = ld.load_century_lis(path + 'flvblackbrush.out/flvblackbrush_Eq_master.lis').loc[5997:5999.92]\n",
"flv_sage = ld.load_century_lis(path + 'flvsagebrush.out/flvsagebrush_Eq_master.lis').loc[5997:5999.92]\n",
"flv_pinyon = ld.load_century_lis(path + 'flvpinyon.out/flvpinyon_Eq_master.lis').loc[5997:5999.92]\n",
"flv_pinyon.tail()"
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DatetimeIndex(['2012-12-31', '2013-01-31', '2013-02-28', '2013-03-31',\n",
" '2013-04-30', '2013-05-31', '2013-06-30', '2013-07-31',\n",
" '2013-08-31', '2013-09-30', '2013-10-31', '2013-11-30',\n",
" '2013-12-31', '2014-01-31', '2014-02-28', '2014-03-31',\n",
" '2014-04-30', '2014-05-31', '2014-06-30', '2014-07-31',\n",
" '2014-08-31', '2014-09-30', '2014-10-31', '2014-11-30',\n",
" '2014-12-31', '2015-01-31', '2015-02-28', '2015-03-31',\n",
" '2015-04-30', '2015-05-31', '2015-06-30', '2015-07-31',\n",
" '2015-08-31', '2015-09-30', '2015-10-31', '2015-11-30'],\n",
" dtype='datetime64[ns]', freq='M')\n"
]
}
],
"source": [
"# Reindex century data with datetime objects\n",
"newix = pd.date_range('2012-12-31', '2015-11-30', freq='M')\n",
"print(newix)\n",
"\n",
"flv_salt.index = newix\n",
"flv_black.index = newix\n",
"flv_sage.index = newix\n",
"flv_pinyon.index = newix"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2012-12-31 NaN\n",
"2013-01-31 36.6745\n",
"2013-02-28 71.1091\n",
"2013-03-31 157.7141\n",
"2013-04-30 51.9424\n",
"Freq: M, Name: resp(1), dtype: float64"
]
},
"execution_count": 185,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get monthly soil resp values from the accumulator\n",
"salt_flv_mon = accum_to_monthly(flv_salt, 'resp(1)')\n",
"black_flv_mon = accum_to_monthly(flv_black, 'resp(1)')\n",
"sage_flv_mon = accum_to_monthly(flv_sage, 'resp(1)')\n",
"pinyon_flv_mon = accum_to_monthly(flv_pinyon, 'resp(1)')\n",
"salt_flv_mon.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CO2 flux plots"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None]"
]
},
"execution_count": 186,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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5wsVSy+ZGR+NS3NSD0gx+CYxuRY8Z3bTjVWzUqFF8++23JCUlERkZyaBBg+yW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33GpUuXaNiwIRqNhmnTprF+/XoaNLhTI3H8+PHY2NjQtm1bWrVqRWxsLNeuXcPLy6tW57ZVVBzs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\ntUIIIYQQovAoqBVCCCGEEIXn/wGSovhLXV13NQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe17e83fba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig1, ax = plt.subplots(4, 2, figsize=(11.5, 8), sharey=True)\n",
"ax[0,0].plot(kc_amund_c.index, kc_amund_c.kc_creosote, marker='o',ls=':')\n",
"#ax[0,0].plot(creo_kc_mon)\n",
"ax[0,0].legend()\n",
"ax[0,0].set_xticklabels([])\n",
"ax[0,0].set_title('Kyle Canyon')\n",
"\n",
"ax[1,0].plot(kc_amund_c.index, kc_amund_c.kc_blackb, marker='o',ls=':')\n",
"ax[1,0].plot(jtree_kc_mon)\n",
"ax[1,0].legend()\n",
"ax[1,0].set_xticklabels([])\n",
"ax[1,0].set_ylabel('gC/m2/month')\n",
"\n",
"ax[2,0].plot(kc_amund_c.index, kc_amund_c.kc_pj, marker='o',ls=':')\n",
"ax[2,0].plot(pj_kc_mon)\n",
"ax[2,0].legend()\n",
"ax[2,0].set_xticklabels([])\n",
"\n",
"ax[3,0].plot(kc_amund_c.index, kc_amund_c.kc_pj, marker=None,color='white')\n",
"plt.setp(ax[3,0].xaxis.get_majorticklabels(), rotation=45)\n",
"\n",
"ax[0,1].plot(flv_oert_c.index, flv_oert_c.flv_salt_1, marker='o',ls=':')\n",
"ax[0,1].plot(flv_oert_c.index, flv_oert_c.flv_salt_2, marker='o',ls=':')\n",
"ax[0,1].plot(flv_oert_c.index, flv_oert_c.flv_salt_3, marker='o',ls=':')\n",
"ax[0,1].plot(salt_flv_mon)\n",
"ax[0,1].legend()\n",
"ax[0,1].set_xticklabels([])\n",
"ax[0,1].set_title('Fish Lake Valley')\n",
"\n",
"ax[1,1].plot(flv_oert_c.index, flv_oert_c.flv_black, marker='o',ls=':')\n",
"ax[1,1].plot(black_flv_mon)\n",
"ax[1,1].legend()\n",
"ax[1,1].set_xticklabels([])\n",
"\n",
"ax[2,1].plot(flv_oert_c.index, flv_oert_c.flv_sage, marker='o',ls=':')\n",
"ax[2,1].plot(sage_flv_mon)\n",
"ax[2,1].legend()\n",
"ax[2,1].set_xticklabels([])\n",
"\n",
"ax[3,1].plot(flv_oert_c.index, flv_oert_c.flv_pinyon, marker='o',ls=':')\n",
"ax[3,1].plot(pinyon_flv_mon)\n",
"ax[3,1].set_ylim([-5,50])\n",
"ax[3,1].legend()\n",
"plt.setp(ax[3,1].xaxis.get_majorticklabels(), rotation=45)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Soil C\n",
"\n",
"## Parse soil survey data"
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user_site_id</th>\n",
" <th>pedon_key</th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" <th>elevation</th>\n",
" <th>precip</th>\n",
" <th>temp</th>\n",
" <th>Total C (kg)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>73NV003001</td>\n",
" <td>73C0105</td>\n",
" <td>35.560833</td>\n",
" <td>115.143056</td>\n",
" <td>1606.0</td>\n",
" <td>227.020803</td>\n",
" <td>13.430305</td>\n",
" <td>4.250039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>73NV003002</td>\n",
" <td>73C0106</td>\n",
" <td>35.565556</td>\n",
" <td>115.089444</td>\n",
" <td>1358.9</td>\n",
" <td>203.577854</td>\n",
" <td>15.136022</td>\n",
" <td>5.961594</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>73NV003003</td>\n",
" <td>73C0107</td>\n",
" <td>35.791667</td>\n",
" <td>115.334167</td>\n",
" <td>895.8</td>\n",
" <td>152.976003</td>\n",
" <td>18.095441</td>\n",
" <td>2.312065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>73NV003004</td>\n",
" <td>73C0108</td>\n",
" <td>35.810556</td>\n",
" <td>115.615278</td>\n",
" <td>809.1</td>\n",
" <td>142.122560</td>\n",
" <td>18.496982</td>\n",
" <td>2.652008</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>73NV003006</td>\n",
" <td>73C0110</td>\n",
" <td>35.991389</td>\n",
" <td>115.263889</td>\n",
" <td>808.7</td>\n",
" <td>141.455012</td>\n",
" <td>18.719576</td>\n",
" <td>1.985300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user_site_id pedon_key lat long elevation precip \\\n",
"0 73NV003001 73C0105 35.560833 115.143056 1606.0 227.020803 \n",
"1 73NV003002 73C0106 35.565556 115.089444 1358.9 203.577854 \n",
"2 73NV003003 73C0107 35.791667 115.334167 895.8 152.976003 \n",
"3 73NV003004 73C0108 35.810556 115.615278 809.1 142.122560 \n",
"4 73NV003006 73C0110 35.991389 115.263889 808.7 141.455012 \n",
"\n",
" temp Total C (kg) \n",
"0 13.430305 4.250039 \n",
"1 15.136022 5.961594 \n",
"2 18.095441 2.312065 \n",
"3 18.496982 2.652008 \n",
"4 18.719576 1.985300 "
]
},
"execution_count": 187,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mojaveSoilC = pd.read_csv('/home/greg/data/rawdata/MojaveCarbon/modeling_background/mojave_soilC.csv')\n",
"mojaveSoilC.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get Century C data and climate"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"16698.5488"
]
},
"execution_count": 188,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flv_salt.somtc.max()"
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/greg/data/miniconda3/lib/python3.5/site-packages/pandas/core/indexing.py:141: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self._setitem_with_indexer(indexer, value)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>siteid_1</th>\n",
" <th>siteid_2</th>\n",
" <th>desc</th>\n",
" <th>lat</th>\n",
" <th>lon</th>\n",
" <th>elev</th>\n",
" <th>WCmat</th>\n",
" <th>WCmap</th>\n",
" <th>PRISMmat</th>\n",
" <th>PRISMmap</th>\n",
" <th>PRISMmap_interp</th>\n",
" <th>MRmodelMAT</th>\n",
" <th>MRmodelMAP</th>\n",
" <th>VegCovPct</th>\n",
" <th>totC_dc</th>\n",
" <th>N2Oflux_dc</th>\n",
" <th>CH4flux_dc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>FLV_A_avg</td>\n",
" <td>flvsaltbush</td>\n",
" <td>Saltbush</td>\n",
" <td>37.838800</td>\n",
" <td>-118.075750</td>\n",
" <td>1482.0</td>\n",
" <td>11.233333</td>\n",
" <td>131.0</td>\n",
" <td>12.28</td>\n",
" <td>125.90</td>\n",
" <td>NaN</td>\n",
" <td>10.629697</td>\n",
" <td>223.786466</td>\n",
" <td>NaN</td>\n",
" <td>16698.5488</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>FLV_B</td>\n",
" <td>flvblackbrush</td>\n",
" <td>Blackbrush</td>\n",
" <td>37.881231</td>\n",
" <td>-118.180439</td>\n",
" <td>1745.0</td>\n",
" <td>9.441667</td>\n",
" <td>157.0</td>\n",
" <td>10.43</td>\n",
" <td>169.56</td>\n",
" <td>NaN</td>\n",
" <td>9.055545</td>\n",
" <td>244.414392</td>\n",
" <td>NaN</td>\n",
" <td>23512.0801</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>FLV_C</td>\n",
" <td>flvsagebrush</td>\n",
" <td>Sagebrush</td>\n",
" <td>37.855369</td>\n",
" <td>-118.230139</td>\n",
" <td>2140.0</td>\n",
" <td>7.033333</td>\n",
" <td>201.0</td>\n",
" <td>7.48</td>\n",
" <td>228.10</td>\n",
" <td>NaN</td>\n",
" <td>6.796285</td>\n",
" <td>276.343843</td>\n",
" <td>NaN</td>\n",
" <td>5844.0327</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>FLV_D</td>\n",
" <td>flvpinyon</td>\n",
" <td>Pinyon</td>\n",
" <td>37.871289</td>\n",
" <td>-118.296381</td>\n",
" <td>2602.0</td>\n",
" <td>3.783333</td>\n",
" <td>296.0</td>\n",
" <td>5.04</td>\n",
" <td>319.64</td>\n",
" <td>NaN</td>\n",
" <td>4.112416</td>\n",
" <td>313.254994</td>\n",
" <td>NaN</td>\n",
" <td>449.5455</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>KC_C</td>\n",
" <td>kccreosote</td>\n",
" <td>Creosote</td>\n",
" <td>36.329600</td>\n",
" <td>-115.295513</td>\n",
" <td>840.0</td>\n",
" <td>17.383333</td>\n",
" <td>115.0</td>\n",
" <td>18.70</td>\n",
" <td>117.28</td>\n",
" <td>NaN</td>\n",
" <td>16.343904</td>\n",
" <td>189.277181</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>KC_B</td>\n",
" <td>kcblackbrush</td>\n",
" <td>Blackbrush</td>\n",
" <td>36.278889</td>\n",
" <td>-115.454877</td>\n",
" <td>1400.0</td>\n",
" <td>13.875000</td>\n",
" <td>199.0</td>\n",
" <td>15.05</td>\n",
" <td>247.32</td>\n",
" <td>NaN</td>\n",
" <td>13.128968</td>\n",
" <td>234.570995</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>KC_P</td>\n",
" <td>kcpj</td>\n",
" <td>PinyonJuniper</td>\n",
" <td>36.269495</td>\n",
" <td>-115.532620</td>\n",
" <td>1750.0</td>\n",
" <td>11.583333</td>\n",
" <td>262.0</td>\n",
" <td>12.87</td>\n",
" <td>330.23</td>\n",
" <td>NaN</td>\n",
" <td>11.106559</td>\n",
" <td>262.699632</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>KC_F</td>\n",
" <td>kcfir</td>\n",
" <td>Fir</td>\n",
" <td>36.262794</td>\n",
" <td>-115.618376</td>\n",
" <td>2150.0</td>\n",
" <td>9.366667</td>\n",
" <td>335.0</td>\n",
" <td>9.29</td>\n",
" <td>553.82</td>\n",
" <td>NaN</td>\n",
" <td>8.792638</td>\n",
" <td>294.813082</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>MNP_Creosote</td>\n",
" <td>mnpcreosote</td>\n",
" <td>Creosote</td>\n",
" <td>35.106130</td>\n",
" <td>-115.550740</td>\n",
" <td>935.4</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>18.00</td>\n",
" <td>178.32</td>\n",
" <td>178.46</td>\n",
" <td>16.766840</td>\n",
" <td>207.908774</td>\n",
" <td>31.7</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>MNP_JTree</td>\n",
" <td>mnpjtree</td>\n",
" <td>Joshua Tree</td>\n",
" <td>35.169120</td>\n",
" <td>-115.480106</td>\n",
" <td>1252.7</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>16.05</td>\n",
" <td>153.44</td>\n",
" <td>155.37</td>\n",
" <td>14.910525</td>\n",
" <td>232.914112</td>\n",
" <td>46.2</td>\n",
" <td>5702.7197</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>MNP_PJ</td>\n",
" <td>mnppj</td>\n",
" <td>Pinyon-juniper</td>\n",
" <td>35.195799</td>\n",
" <td>-115.347572</td>\n",
" <td>1601.1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>13.50</td>\n",
" <td>204.62</td>\n",
" <td>205.33</td>\n",
" <td>12.923108</td>\n",
" <td>260.824650</td>\n",
" <td>50.8</td>\n",
" <td>4234.3379</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>MNP_Q6</td>\n",
" <td>mnpq6</td>\n",
" <td>Q6 Creosote</td>\n",
" <td>34.904885</td>\n",
" <td>-115.639693</td>\n",
" <td>880.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>18.20</td>\n",
" <td>156.52</td>\n",
" <td>NaN</td>\n",
" <td>17.231973</td>\n",
" <td>205.205133</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>MNP_Q4</td>\n",
" <td>mnpq4</td>\n",
" <td>Q4 Creosote</td>\n",
" <td>34.899392</td>\n",
" <td>-115.636695</td>\n",
" <td>905.6</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>18.03</td>\n",
" <td>155.09</td>\n",
" <td>NaN</td>\n",
" <td>17.090461</td>\n",
" <td>207.316776</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>MNP_Q3</td>\n",
" <td>mnpq3</td>\n",
" <td>Q3 Creosote</td>\n",
" <td>34.905177</td>\n",
" <td>-115.611360</td>\n",
" <td>999.5</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>17.93</td>\n",
" <td>154.93</td>\n",
" <td>NaN</td>\n",
" <td>16.553223</td>\n",
" <td>214.840122</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" siteid_1 siteid_2 desc lat lon \\\n",
"0 FLV_A_avg flvsaltbush Saltbush 37.838800 -118.075750 \n",
"1 FLV_B flvblackbrush Blackbrush 37.881231 -118.180439 \n",
"2 FLV_C flvsagebrush Sagebrush 37.855369 -118.230139 \n",
"3 FLV_D flvpinyon Pinyon 37.871289 -118.296381 \n",
"4 KC_C kccreosote Creosote 36.329600 -115.295513 \n",
"5 KC_B kcblackbrush Blackbrush 36.278889 -115.454877 \n",
"6 KC_P kcpj PinyonJuniper 36.269495 -115.532620 \n",
"7 KC_F kcfir Fir 36.262794 -115.618376 \n",
"8 MNP_Creosote mnpcreosote Creosote 35.106130 -115.550740 \n",
"9 MNP_JTree mnpjtree Joshua Tree 35.169120 -115.480106 \n",
"10 MNP_PJ mnppj Pinyon-juniper 35.195799 -115.347572 \n",
"11 MNP_Q6 mnpq6 Q6 Creosote 34.904885 -115.639693 \n",
"12 MNP_Q4 mnpq4 Q4 Creosote 34.899392 -115.636695 \n",
"13 MNP_Q3 mnpq3 Q3 Creosote 34.905177 -115.611360 \n",
"\n",
" elev WCmat WCmap PRISMmat PRISMmap PRISMmap_interp MRmodelMAT \\\n",
"0 1482.0 11.233333 131.0 12.28 125.90 NaN 10.629697 \n",
"1 1745.0 9.441667 157.0 10.43 169.56 NaN 9.055545 \n",
"2 2140.0 7.033333 201.0 7.48 228.10 NaN 6.796285 \n",
"3 2602.0 3.783333 296.0 5.04 319.64 NaN 4.112416 \n",
"4 840.0 17.383333 115.0 18.70 117.28 NaN 16.343904 \n",
"5 1400.0 13.875000 199.0 15.05 247.32 NaN 13.128968 \n",
"6 1750.0 11.583333 262.0 12.87 330.23 NaN 11.106559 \n",
"7 2150.0 9.366667 335.0 9.29 553.82 NaN 8.792638 \n",
"8 935.4 NaN NaN 18.00 178.32 178.46 16.766840 \n",
"9 1252.7 NaN NaN 16.05 153.44 155.37 14.910525 \n",
"10 1601.1 NaN NaN 13.50 204.62 205.33 12.923108 \n",
"11 880.0 NaN NaN 18.20 156.52 NaN 17.231973 \n",
"12 905.6 NaN NaN 18.03 155.09 NaN 17.090461 \n",
"13 999.5 NaN NaN 17.93 154.93 NaN 16.553223 \n",
"\n",
" MRmodelMAP VegCovPct totC_dc N2Oflux_dc CH4flux_dc \n",
"0 223.786466 NaN 16698.5488 NaN NaN \n",
"1 244.414392 NaN 23512.0801 NaN NaN \n",
"2 276.343843 NaN 5844.0327 NaN NaN \n",
"3 313.254994 NaN 449.5455 NaN NaN \n",
"4 189.277181 NaN NaN NaN NaN \n",
"5 234.570995 NaN NaN NaN NaN \n",
"6 262.699632 NaN NaN NaN NaN \n",
"7 294.813082 NaN NaN NaN NaN \n",
"8 207.908774 31.7 NaN NaN NaN \n",
"9 232.914112 46.2 5702.7197 NaN NaN \n",
"10 260.824650 50.8 4234.3379 NaN NaN \n",
"11 205.205133 NaN NaN NaN NaN \n",
"12 207.316776 NaN NaN NaN NaN \n",
"13 214.840122 NaN NaN NaN NaN "
]
},
"execution_count": 189,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('/home/greg/data/gdrive_berkeley/MojaveCarbon/Data/mojave_field_sites.csv')\n",
"\n",
"df['totC_dc'] = np.nan\n",
"df['N2Oflux_dc'] = np.nan\n",
"df['CH4flux_dc'] = np.nan\n",
"df.totC_dc.loc[[0, 1, 2, 3, 9, 10]] = [flv_salt.somtc.max(),flv_black.somtc.max(),\n",
" flv_sage.somtc.max(),flv_pinyon.somtc.max(),\n",
" mnp_jtree.somtc.max(),mnp_pj.somtc.max()]\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fe17ff592b0>"
]
},
"execution_count": 190,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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a7777Lm+++SaZmZlMnz6dlStXcvnll/Pss8/y6quv8vDDD/PQQw+xZMkSatas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\njYqiKIriN/4/pOG1VTtJKRsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe17edc0630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig2, ax = plt.subplots()\n",
"ax.scatter(mojaveSoilC.precip, mojaveSoilC['Total C (kg)']*1000, label='Mojave Soil Survey C', s=10)\n",
"ax.plot(df.PRISMmap.iloc[0], df.totC_dc.iloc[0], marker='s', color='green',label='DayCent FLV Saltbush')\n",
"ax.plot(df.PRISMmap.iloc[1], df.totC_dc.iloc[1], marker='s', color='black',label='DayCent FLV Blackbrush')\n",
"ax.plot(df.PRISMmap.iloc[2], df.totC_dc.iloc[2], marker='s', color='purple',label='DayCent FLV Sagebrush')\n",
"ax.plot(df.PRISMmap.iloc[3], df.totC_dc.iloc[3], marker='s', color='magenta',label='DayCent FLV Pinyon')\n",
"#ax.plot(df.PRISMmap.iloc[8], df.totC_dc.iloc[4], marker='s', color='y',label='DayCent Creosote')\n",
"ax.plot(df.PRISMmap.iloc[9], df.totC_dc.iloc[9], marker='s', color='orange',label='DayCent JTree')\n",
"ax.plot(df.PRISMmap.iloc[10], df.totC_dc.iloc[10], marker='s', color='red',label='DayCent PJ')\n",
"#ax.plot(df.MAP, df.CentTotC, marker='o', color='y',label='Creosote')\n",
"ax.set_ylabel('Soil C (g)')\n",
"ax.set_xlabel('MAP (mm)')\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fe18003f390>"
]
},
"execution_count": 191,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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DLOn677vvPnJycnj//fe9Tte/YsUKnn32WQCX6foBe7r+hg0blvk6rr/+enti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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe17f845908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig3, ax = plt.subplots()\n",
"ax.scatter(mojaveSoilC.temp, mojaveSoilC['Total C (kg)']*1000, label='Mojave Soil Survey C',s=10)\n",
"ax.plot(df.PRISMmat.iloc[0], df.totC_dc.iloc[0], marker='s', color='green',label='DayCent FLV Saltbush')\n",
"ax.plot(df.PRISMmat.iloc[1], df.totC_dc.iloc[1], marker='s', color='black',label='DayCent FLV Blackbrush')\n",
"ax.plot(df.PRISMmat.iloc[2], df.totC_dc.iloc[2], marker='s', color='purple',label='DayCent FLV Sagebrush')\n",
"ax.plot(df.PRISMmat.iloc[3], df.totC_dc.iloc[3], marker='s', color='magenta',label='DayCent FLV Pinyon')\n",
"#ax.plot(df.PRISMmap.iloc[8], df.totC_dc.iloc[4], marker='s', color='y',label='DayCent Creosote')\n",
"ax.plot(df.PRISMmat.iloc[9], df.totC_dc.iloc[9], marker='s', color='orange',label='DayCent JTree')\n",
"ax.plot(df.PRISMmat.iloc[10], df.totC_dc.iloc[10], marker='s', color='red',label='DayCent PJ')\n",
"ax.set_ylabel('Soil C (g)')\n",
"ax.set_xlabel('MAT (C)')\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Trace gasses"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {},
"outputs": [],
"source": [
"path = '/home/greg/current/mojaveCENT/DAYCENT_flv/'\n",
"flv_salt_ann = pd.read_fwf(path + 'flvsaltbush.out/year_summary_master.out')\n",
"flv_black_ann = pd.read_fwf(path + 'flvblackbrush.out/year_summary_master.out')\n",
"flv_sage_ann = pd.read_fwf(path + 'flvsagebrush.out/year_summary_master.out')\n",
"flv_pinyon_ann = pd.read_fwf(path + 'flvpinyon.out/year_summary_master.out')"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {},
"outputs": [],
"source": [
"df.index = df.siteid_2\n",
"df.loc['flvsaltbush', 'N2Oflux'] = flv_salt_ann.N2Oflux.max() * 10000\n",
"df.loc['flvblackbrush', 'N2Oflux'] = flv_black_ann.N2Oflux.max() * 10000\n",
"df.loc['flvsagebrush', 'N2Oflux'] = flv_sage_ann.N2Oflux.max() * 10000\n",
"df.loc['flvpinyon', 'N2Oflux'] = flv_pinyon_ann.N2Oflux.max() * 10000\n",
"\n",
"df.loc['flvsaltbush', 'CH4flux'] = flv_salt_ann.N2Oflux.max() * 10000\n",
"df.loc['flvblackbrush', 'CH4flux'] = flv_black_ann.N2Oflux.max() * 10000\n",
"df.loc['flvsagebrush', 'CH4flux'] = flv_sage_ann.N2Oflux.max() * 10000\n",
"df.loc['flvpinyon', 'CH4flux'] = flv_pinyon_ann.N2Oflux.max() * 10000"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fe17ef404e0>"
]
},
"execution_count": 194,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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T5oXlwPPy8qLlwEtq1qwZ/fv3p127dpx++unRIaz8/HwGDBjAN998w8GDB7nvvvvi+n5E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\nkUBKECIiEuj/AbfAEutbSO5MAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe17e926ef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig4, ax = plt.subplots()\n",
"#ax.scatter(mojaveSoilC.temp, mojaveSoilC['Total C (kg)']*1000, label='Mojave Soil Survey C',s=10)\n",
"ax.plot(df.PRISMmat.iloc[0], df.N2Oflux.iloc[0], marker='s', color='green',label='DayCent FLV Saltbush')\n",
"ax.plot(df.PRISMmat.iloc[1], df.N2Oflux.iloc[1], marker='s', color='black',label='DayCent FLV Blackbrush')\n",
"ax.plot(df.PRISMmat.iloc[2], df.N2Oflux.iloc[2], marker='s', color='purple',label='DayCent FLV Sagebrush')\n",
"ax.plot(df.PRISMmat.iloc[3], df.N2Oflux.iloc[3], marker='s', color='magenta',label='DayCent FLV Pinyon')\n",
"ax.set_ylabel('N2O flux (gN/ha)')\n",
"ax.set_xlabel('MAT (C)')\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fe17efb6ba8>"
]
},
"execution_count": 195,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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mVtIFc1OBzEJt44DX3b0V8HrkNYTvO9Eq8hgDPFTCvkVEpALFOotpFfCSme0B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\nJJAShIiIBPr/948tnodfSvgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe17fd47048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig5, ax = plt.subplots()\n",
"#ax.scatter(mojaveSoilC.temp, mojaveSoilC['Total C (kg)']*1000, label='Mojave Soil Survey C',s=10)\n",
"ax.plot(df.PRISMmat.iloc[0], df.CH4flux.iloc[0], marker='s', color='green',label='DayCent FLV Saltbush')\n",
"ax.plot(df.PRISMmat.iloc[1], df.CH4flux.iloc[1], marker='s', color='black',label='DayCent FLV Blackbrush')\n",
"ax.plot(df.PRISMmat.iloc[2], df.CH4flux.iloc[2], marker='s', color='purple',label='DayCent FLV Sagebrush')\n",
"ax.plot(df.PRISMmat.iloc[3], df.CH4flux.iloc[3], marker='s', color='magenta',label='DayCent FLV Pinyon')\n",
"ax.set_ylabel('CH4 flux (gC/ha)')\n",
"ax.set_xlabel('MAT (C)')\n",
"plt.legend()"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"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": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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