plot_annual_fpari.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from fpar_workflow import *\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.stats.api as sms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  4.01515429e-01,   2.92958218e-01,   3.62597255e-01,\n",
       "          5.44431247e-15],\n",
       "       [  4.19026062e-01,   2.16592270e-01,   2.30405792e-02,\n",
       "          1.20001916e-15],\n",
       "       [  3.06336596e-01,   1.68920306e-01,   1.62997093e-01,\n",
       "          4.71919945e-17],\n",
       "       ..., \n",
       "       [  3.07608029e-01,  -1.13777492e-01,   6.05554908e-02,\n",
       "          6.12955128e-16],\n",
       "       [  1.05789967e-02,  -1.87161042e-01,  -1.54277993e-02,\n",
       "          6.73293263e-16],\n",
       "       [  1.45582501e-01,  -1.28654642e-01,   1.76029396e-02,\n",
       "          6.35065462e-16]])"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA()\n",
    "X_reduced = pca.fit_transform(fp_workflow.cdl_matrix)\n",
    "X_reduced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fp_workflow = Fpar_flow()\n",
    "p4 = (41.53067026099156,-90.31086962890625)\n",
    "p3 = (41.53067026099156, -90.29682275390625)\n",
    "p2 = (41.55638178482896,-90.29682275390625)\n",
    "p1 = (41.55638178482896, -90.3186962890625)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fpar_path = 'FPAR_A2016185.hdf'\n",
    "cdl_path = 'cdl_chicago.npy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fp_workflow.cdl_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1552, 6275) (1552, 6279)\n",
      "(1557, 6279) (1557, 6277)\n",
      "fuckit\n",
      "(array([  85.7710763 ,   81.01829318,  106.00607548,   66.74423031]), array([ 778.96294232]), 4, array([ 2.42635342,  1.18245225,  0.56001946,  0.34958254]))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bowen\\Desktop\\work\\Satellite_Big_Data_Scripts\\fpar_workflow.py:56: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
      "  assert self.cdl_matrix != None and self.fpar_vals != None\n"
     ]
    }
   ],
   "source": [
    "fp_workflow.init_workflow(fpar_path, cdl_path, p1, p2, p3, p4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1549, 6275) (1549, 6572)\n",
      "(1563, 6572) (1563, 6277)\n",
      "fuckit\n",
      "(array([ 67.14582447,  68.10530203,  72.832421  ,  68.15809251]), array([ 192351.69219643]), 4, array([ 41.02761123,  21.67232743,  13.16324277,   5.07118152]))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bowen\\Desktop\\work\\Satellite_Big_Data_Scripts\\fpar_workflow.py:56: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
      "  assert self.cdl_matrix != None and self.fpar_vals != None\n"
     ]
    }
   ],
   "source": [
    "fp_workflow.continue_workflow(fpar_path, p1, p2, p3, p4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\bowen\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1390: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=15\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.188</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.8503</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 19 Mar 2018</td> <th>  Prob (F-statistic):</th>  <td> 0.495</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:43:53</td>     <th>  Log-Likelihood:    </th> <td> -50.908</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    15</td>      <th>  AIC:               </th> <td>   109.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    11</td>      <th>  BIC:               </th> <td>   112.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td>   85.7711</td> <td>    4.411</td> <td>   19.444</td> <td> 0.000</td> <td>   76.062</td> <td>   95.480</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th> <td>   81.0183</td> <td>   14.778</td> <td>    5.483</td> <td> 0.000</td> <td>   48.493</td> <td>  113.543</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th> <td>  106.0061</td> <td>   22.704</td> <td>    4.669</td> <td> 0.001</td> <td>   56.035</td> <td>  155.977</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th> <td>   66.7442</td> <td>   10.707</td> <td>    6.234</td> <td> 0.000</td> <td>   43.179</td> <td>   90.310</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.192</td> <th>  Durbin-Watson:     </th> <td>   2.373</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.908</td> <th>  Jarque-Bera (JB):  </th> <td>   0.350</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.202</td> <th>  Prob(JB):          </th> <td>   0.839</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.370</td> <th>  Cond. No.          </th> <td>    6.94</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.188\n",
       "Model:                            OLS   Adj. R-squared:                 -0.033\n",
       "Method:                 Least Squares   F-statistic:                    0.8503\n",
       "Date:                Mon, 19 Mar 2018   Prob (F-statistic):              0.495\n",
       "Time:                        17:43:53   Log-Likelihood:                -50.908\n",
       "No. Observations:                  15   AIC:                             109.8\n",
       "Df Residuals:                      11   BIC:                             112.6\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1            85.7711      4.411     19.444      0.000      76.062      95.480\n",
       "x2            81.0183     14.778      5.483      0.000      48.493     113.543\n",
       "x3           106.0061     22.704      4.669      0.001      56.035     155.977\n",
       "x4            66.7442     10.707      6.234      0.000      43.179      90.310\n",
       "==============================================================================\n",
       "Omnibus:                        0.192   Durbin-Watson:                   2.373\n",
       "Prob(Omnibus):                  0.908   Jarque-Bera (JB):                0.350\n",
       "Skew:                          -0.202   Prob(JB):                        0.839\n",
       "Kurtosis:                       2.370   Cond. No.                         6.94\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/AbwJ+EhVXTvomiRJs9f1NY4LgO9X1Q87rkOSNEtdB8dlwE1TXr8zyd1Jrk9yfFdFSZJm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e8ba08710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hat = fp_workflow.cdl_matrix@fp_workflow.reg_coeffs\n",
    "\n",
    "y = fp_workflow.fpar_vals\n",
    "x = fp_workflow.cdl_matrix\n",
    "\n",
    "\n",
    "\n",
    "plt.xlim(70, 100)\n",
    "plt.ylim(70, 100)\n",
    "plt.scatter(y_hat, y)\n",
    "plt.xlabel('predicted')\n",
    "# xlim((70,100))\n",
    "# ylim((70, 100))\n",
    "plt.ylabel('actual')\n",
    "\n",
    "\n",
    "# yx = np.append(x, np.array(y).reshape(-1,1), axis = 1)\n",
    "# yx[:,:-1]\n",
    "\n",
    "# sampleyx = yx[np.random.choice(yx.shape[0], 1000, replace=False)]\n",
    "# SST = np.sum((y - np.mean(y))**2)\n",
    "# SSE = np.sum((y - y_hat)**2)\n",
    "# SSR = np.sum((y_hat - np.mean(y))**2)\n",
    "# R_square = 1 - SSE/SST\n",
    "# print(R_square)\n",
    "\n",
    "X_reduced = sm.add_constant(X_reduced)\n",
    "model = sm.OLS(y, x).fit()\n",
    "model.summary()\n",
    "# np.linalg.cond(model.model.exog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.280</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.280</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   318.4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 19 Mar 2018</td> <th>  Prob (F-statistic):</th> <td>1.47e-174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:19:09</td>     <th>  Log-Likelihood:    </th> <td>  435.20</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  2454</td>      <th>  AIC:               </th> <td>  -864.4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  2451</td>      <th>  BIC:               </th> <td>  -847.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td>    0.0708</td> <td>    0.008</td> <td>    9.383</td> <td> 0.000</td> <td>    0.056</td> <td>    0.086</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th> <td>    0.0598</td> <td>    0.012</td> <td>    5.181</td> <td> 0.000</td> <td>    0.037</td> <td>    0.082</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th> <td>    1.0093</td> <td>    0.045</td> <td>   22.636</td> <td> 0.000</td> <td>    0.922</td> <td>    1.097</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1162.609</td> <th>  Durbin-Watson:     </th> <td>   1.167</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>6112.496</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 2.259</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 9.275</td>  <th>  Cond. No.          </th> <td>    6.96</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.280\n",
       "Model:                            OLS   Adj. R-squared:                  0.280\n",
       "Method:                 Least Squares   F-statistic:                     318.4\n",
       "Date:                Mon, 19 Mar 2018   Prob (F-statistic):          1.47e-174\n",
       "Time:                        17:19:09   Log-Likelihood:                 435.20\n",
       "No. Observations:                2454   AIC:                            -864.4\n",
       "Df Residuals:                    2451   BIC:                            -847.0\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1             0.0708      0.008      9.383      0.000       0.056       0.086\n",
       "x2             0.0598      0.012      5.181      0.000       0.037       0.082\n",
       "x3             1.0093      0.045     22.636      0.000       0.922       1.097\n",
       "==============================================================================\n",
       "Omnibus:                     1162.609   Durbin-Watson:                   1.167\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             6112.496\n",
       "Skew:                           2.259   Prob(JB):                         0.00\n",
       "Kurtosis:                       9.275   Cond. No.                         6.96\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x3 = fp_workflow.cdl_matrix[:,3]\n",
    "x12 = fp_workflow.cdl_matrix[:,[0,1,2]]\n",
    "# x12 = sm.add_constant(x12)\n",
    "collinearity = sm.OLS(x3, x12).fit()\n",
    "collinearity.summary()\n",
    "# x12\n",
    "# x12\n",
    "# np.sum(x12, axis = 1) + x3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'sklearn' has no attribute 'Linear_model'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-130-27e925464154>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;31m# from sklearn.linear_model import Ridge\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLinear_model\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: module 'sklearn' has no attribute 'Linear_model'"
     ]
    }
   ],
   "source": [
    "# from sklearn.linear_model import Ridge\n",
    "import sklearn\n",
    "sklearn.Linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2e8b891b70>"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Elfe2b4Cds2HuaGP2toGfydSEJpFCL0QZkZCeybzticzZdoK9p87j7qZoHx3E\nnU3D6NGgqv167qTGw8xhcCrOONmq2z+lKcfBpNALUQbtO3WeOdtOMGfbSU6cuYiPpxvd61VhQNNw\nOtcOxsvDxqfO5GbB4pdg05dQrTXcMwUqRdh2H+K6pNALUYZZLJotx9KZs+0kv+44SXpmrrXnTigD\nmobRKtLGPXfiZsG8McYR/cDJxtg5wu6k0AshAGvPnQMp/LLtBL/tSuJibj6hlXzob/0Rt36ojXru\npMbDjGHGdJ0yd7JDSKEXQvzFpZ47c7adZKW1505MSHkGNA1jQNNwqgX4lmwHuReNAdE2T4VqbaxN\nOeG2CS/+Qgq9EOKG0i733DnBxiPpADSr7s+ApuHc0SSMAL8SDHdwuSnHC+6aDDG32yi1KEgKvRCi\nyBLSM5m7/SRzt51k76nzeLgputQJZmBsBN3qheDjWYzhF1IOGCdYJe2EDs9A11fAvQxO12hHUuiF\nEMWyJ/EcP289wS9bT3D6fDYVfDzo1ziUgbERtKhR+dZ+xM29CAtfgC3ToHpboymnYpj9wpcxUuiF\nECWSb9GsjU9h9pYTLNp5iou5+URULsfA2HAGxoZTM7h80Te2YwbMGwuePkZTTnR3+wUvQ6TQCyFs\nJiM7j8W7TvHz1hOsOZiCRUPTav7c1Sycfo2L2J6fvN84wer0buj4LHR5SZpySkgKvRDCLpLOZTFn\n2wlmbzlRoD0/hLuahXNb3Zu05+dkwsLnYes3UKO9MSl5xVDHhXcxUuiFEHa3++Q5ft6awJxtJzl9\nPpuKPh70bRzGXc3CaVGj8vX752+fDr8+A56+1qacbo4N7iKk0AshHCbfollzMIWft15pz68WUI6B\nTcMZ2CyCqKBCRtZM3mecYJW8FzqNgy4vGoOkiSKTQi+EMEVGdh6Ldlrb8+NT0NoYWfOuWKM9v3LB\n9vycTFj4HGz9FiI7Gr1yyoeYF76UkUIvhDDdqbNX2vP3JZ3H093anh8bzm31Qq5Mj7jtB6Mpp1pL\nGDoXHDWZSiknhV4I4TS01uxOPMfPW04wZ/tJkq3t+f2ahHFXbDjNa1RGbZoC8/8OAz6C2MFmRy4V\npNALIZxSXr6FNfGp/LwlgcXWQdaqB/hyZ9NQRh8djUfqPnhqE5QPNjuq0ytqoZdOrEIIh/Jwd6Nz\n7WA61w7mwuX2/AQ+WB5PWtSjTMx5DBaNh3u+NDuqy7DxLARCCFF05b09uKd5BN893Ibne9bl20Pl\nONbgcdg5Cw4sMTuey5BCL4RwCiPaRxLuX46njndFB9WGX/8O2RfMjuUSpNALIZyCj6c743vXZcep\niyyv/QqcPQbL/2N2LJcghV6geQq7AAATY0lEQVQI4TT6NQ4ltro/4zf6kRs7HNZ/Aie2mB2r1JNC\nL4RwGkopXulbn9Pns/ncayj4hcC80ZCfa3a0Uk0KvRDCqTSvUZm+jUN5f+1p0ru8BqfiYN3HZscq\n1aTQCyGczvhedbFY4LVD0VCnLyx/HdIOmx2r1CpRoVdK+SulZiml9iql9iil2iqlApRSS5RSB6zX\nlW0VVghRNlQL8GVE+0h+2nqCvc3+CW4e8OtYcIITPEujkh7R/w9YpLWuCzQB9gDjgWVa6xhgmfW+\nEELckie6RlPZ14sJf6Sju/0TDq2AHT+aHatUKnahV0pVBDoBXwJorXO01meAAcA062rTgDtLGlII\nUfZUKufJM91jWHcojaV+/SCiJSx6ETJSzI5W6pTkiL4mkAxMVUptVUp9oZTyA6porRMBrNcy5qgQ\nolgeaFWdWsF+vL5oP7l934Ps87D4ZbNjlTolKfQeQDPgE611LJDBLTTTKKUeVUptUkptSk5OLkEM\nIYSr8nB34+W+9TiUksF3h/ygw1jYMR0OLjM7WqlSkkKfACRorddb78/CKPxJSqlQAOv16cKerLWe\nrLVuobVuERwso9QJIQrXtU4I7aMDeW/ZAc62GAOB0cbY9TkZZkcrNYpd6LXWp4DjSqk61kXdgN3A\nXGCYddkwYE6JEgohyjSlFC/3qc/Zi7l8uOo43PE/OHMUVrxhdrRSo6S9bp4GvlNK7QCaAv8B3gBu\nV0odAG633hdCiGKrH1aRQc0j+GrtEY5WiIVmQ+HPjyBxu9nRSoUSFXqt9TZr80tjrfWdWut0rXWq\n1rqb1jrGep1mq7BCiLLr2R518HR3442Fe+H2f4FvIMwdDfl5ZkdzenJmrBCiVKhS0YfHOtdi4c5T\nbEzS0PtNSNwG6z81O5rTk0IvhCg1HulYk6oVfZj4624s9e6EmJ6w/DVIP2J2NKcmhV4IUWqU83Ln\nuZ512J5wlnlxidD3HVBuMP9ZGR7hBqTQCyFKlYGx4TQMr8ibC/eS5RcGt/0DDi6FuFlmR3NaUuiF\nEKWKm5vR3fLk2Sy+XH0YWj0C4c2NCcUzpe9HYaTQCyFKnba1Arm9fhU+Xn6Q5Iw8uON9yDoDv71i\ndjSnJIVeCFEqvdi7Ltl5Ft5dsh+qNoR2T8O274xRLsVVpNALIUqlmsHlGdK2Bj9uPMa+U+eh8wsQ\nUBPmjYXci2bHcypS6IUQpdaYbjGU9/bgtQV7wLMc9HsP0g/DH2+aHc2pSKEXQpRa/r5ejO4Ww8r9\nyazYdxpqdoamD8Ga9+HUTrPjOQ0p9EKIUm1o20hqBPrynwV7yMu3QI+JUK4yzH0aLPlmx3MKUuiF\nEKWal4cbL/auy/6kC8zYlAC+AcbwCCe3wIbJZsdzClLohRClXs8GVWkVGcC7S/ZxPisXGt4N0d1h\n2b/hzHGz45lOCr0QotRTSvFKv3qkXMjhkxXxoBT0fRfQMjwCUuiFEC6icYQ/A2PD+WL1YRLSM6Fy\nDej6MhxYDLt+NjueqaTQCyFcxnM966CASYv3GQtaPwahTWHhC3Ax3dRsZpJCL4RwGWH+5XikY03m\nbDvJtuNnwN0D+r8Pmanw2z/MjmcaKfRCCJfyWJdaBJX3ZuKvu9FaQ2gTaPskbP0GDq8yO54ppNAL\nIVxKeW8Pnu1Rm01H01m085SxsMuL4F8Dfh0LuVnmBjSBFHohhMu5t0U16latwOsL95Kdlw9evnDH\ne5B6EFa9bXY8h5NCL4RwOe5uipf61ONYWiZfrz1qLKx1GzS+H1b/F5J2mxvQwaTQCyFcUqfawXSp\nE8z7vx8gLSPHWNjzP+BdEeaNLlPDI0ihF0K4rJf61CMjO4/3lx0wFvgFQq/XIWEjbJpibjgHkkIv\nhHBZtatU4IFW1fl23VHiky8YCxvfBzW7wtJX4ewJcwM6iBR6IYRLe+b22vh4uvP6gr3GAqWg33/B\nkgcLxpWJ4RGk0AshXFpQeW+e6FqLpXuSWBufYiwMiIKuL8K+BbBnrrkBHUAKvRDC5Y1sH0W4fzkm\n/rqHfIv1CL7Nk1C1ESx4Di6eMTegnUmhF0K4PB9Pd57vVYfdieeYvSXBWOjuAf0/gIxkWPp/5ga0\nMyn0QogyoX+TMJpW8+ft3/aRmZNnLAyLNQY+2zwNkveZG9COpNALIcoEpRT/6FePpHPZTF556MoD\nHceBpy+snGReODuTQi+EKDOa1wigb6NQPvvjEEnnrGPe+AVCy1Gw8ydIOWBuQDuRQi+EKFNe6FWX\nfIvm7cUFmmrajQZ3b1jpmuPgSKEXQpQp1QN9Gd4+kllbEth18qyxsHywcVQfNwNS480NaAdS6IUQ\nZc6TXaPxL+fJa/P3GGPWg/Wo3gtWvWNuODuQQi+EKHMqlfNkbPfarI1PZdme08bCClWg+QjYPh3S\nDpsb0Mak0AshyqQHW1enZrAf/1m4h9x8i7Gw/Rhw83C5o3op9EKIMsnT3Y2XetfjUHIG368/Ziys\nGArNh8H2HyD9qLkBbajEhV4p5a6U2qqU+tV6P0optV4pdUAp9aNSyqvkMYUQwva61QuhXa1AJs7f\nzaivNvLT5gTONX8KlBusftfseDZjiyP6McCeAvffBP6rtY4B0oFRNtiHEELYnFKK9+5rytC2kexJ\nPMezM7fT/P1d/O7bE8uWbzl3yjXa6ktU6JVSEUBf4AvrfQXcBsyyrjINuLMk+xBCCHsKqejDP/rV\nZ8342/j5iXYMbxfJhzl3kGfRzPtoHMOnbmDGpuOcycwxO2qxeZTw+e8BzwMVrPcDgTNaa+tAEiQA\n4SXchxBC2J1SitjqlYmtXhndpx6p01dw3/6ZzEq6j+f3JfOSm6JddBB9G1WlR/2qVPYrPa3SxT6i\nV0r1A05rrTcXXFzIqoWO6q+UelQptUkptSk5Obm4MYQQwuaUUgT1Go+H0sxutIG5T7VnVMcoDqdc\n4IWf4mjx2lKGfLmeHzYcuzIfrRNTupizqyilXgeGAHmAD1AR+BnoCVTVWucppdoCE7TWPW+0rRYt\nWuhNmzYVK4cQQtjNnKdgxwwYsx0qhqK1ZueJcyzYmciCuESOpmbi7qZoWzOQPo1C6dmgCoHlvR0W\nTym1WWvd4qbrFbfQX7OzLsA4rXU/pdRM4Cet9XSl1KfADq31xzd6vhR6IYRTSjsMHzSH1n8zJhUv\nQGvNrpPnWBBnFP0jqZm4KWhjLfq9GlYlyM5F38xCXxOYDgQAW4HBWuvsGz1fCr0Qwmn98oQxsuWY\nHcbZs4XQWrMn8fzlon8oJQM3Ba2ijNEyezasSkgFH5tHc2ihLykp9EIIp5UaDx+2gDZPQM/Xbrq6\n1pq9p86zMC6R+XGJxCdnoBS0igygT6NQejesSkhF2xR9KfRCCGErs/8Gu+fA2DhjpMsi0lqzP+kC\n861H+gdPX0ApaFkjgD6NqtK7UShVSlD0pdALIYStpByAj1pBu6fh9n8VezMHks5fLvr7k4yi/6/+\nDRjSNrJY2ytqoS9pP3ohhHB9QTHQ8G7Y8AW0G2PMSlUMMVUqMLZKBcZ2r83B0+dZEHeKVlHF29at\nkEHNhBCiKDo9B7mZ8OeHNtlcdEgFRneLoU7VCjdfuYSk0AshRFEE14EGA2HDZMhMMzvNLZFCL4QQ\nRdXpOci5AOtueGqQ05FCL4QQRVWlPtQfAOs/g4vpZqcpMin0QghxKzo9D9nnYN2nZicpMin0Qghx\nK6o2hLr9YN0ncPGM2WmKRAq9EELcqs7PQ/ZZ44fZUkAKvRBC3KrQJlCnD/z5EWSdMzvNTUmhF0KI\n4uj8PGSdKRVH9VLohRCiOMJiIaancQJV9nmz09yQFHohhCiuzi8Y3Sw3fmF2khuSQi+EEMUV0Ryi\nu8PaDyAnw+w01yWFXgghSqLzC5CZChu/NDvJdUmhF0KIkqjWCmp2hbXvQ06m2WkKJYVeCCFKqvML\nkJEMm6eanaRQUuiFEKKkarSFqE6w5n+Qe9HsNH8hhV4IIWyh8wtwIQk2TzM7yV9IoRdCCFuI7AA1\nOsCa9yA3y+w0V5FCL4QQttL5eTifCFu/MTvJVaTQCyGErUR1gmptYPV/IS/b7DSXSaEXQghbUQq6\nvADnTsDWb81Oc5kUeiGEsKWaXSGipfWoPsfsNIAUeiGEsC2loPN4OHsctv9gdhpACr0QQthedDcI\nawar3ob8XLPTSKEXQgibUwq6jIczx2DHj2ankUIvhBB2EdMDQpvCyrchP8/UKFLohRDCHpQyzpZN\nPwxxM02NIoVeCCHspU5vqNoIVk4y9aheCr0QQtjLpaP6tHjYNdu0GFLohRDCnur0hZAG8MdbYMk3\nJYIUeiGEsCc3N2MMnNQDsOtncyKYslchhChL6vWH4HpGW73F4vDdS6EXQgh7c3ODzs9B8l7YM8fx\nu3f4HoUQoiyqfycE1ba21Tv2qL7YhV4pVU0ptVwptUcptUspNca6PEAptUQpdcB6Xdl2cYUQopRy\nc4dOz8Pp3bD3V8fuugTPzQOe1VrXA9oATyql6gPjgWVa6xhgmfW+EEKIhndBYLTDj+qLXei11ola\n6y3W2+eBPUA4MAC4NGniNODOkoYUQgiX4OYOnZ6DpDjYv9Bxu7XFRpRSkUAssB6oorVOBOPDAAi5\nznMeVUptUkptSk5OtkUMIYRwfg3vgcpR8MeboLVDdlniQq+UKg/8BIzVWp8r6vO01pO11i201i2C\ng4NLGkMIIUoHdw/oNA4St8P+xQ7ZZYkKvVLKE6PIf6e1vnR+b5JSKtT6eChwumQRhRDCxTS+D/xr\nOOyoviS9bhTwJbBHa/1ugYfmAsOst4cBju80KoQQzszdEzo+Cye3wMGldt+dRwme2x4YAsQppbZZ\nl70EvAHMUEqNAo4Bg0oWUQghXFCTB4xulu6edt9VsQu91no1oK7zcLfiblcIIcoEDy94yDHj1MuZ\nsUII4eKk0AshhIuTQi+EEC5OCr0QQrg4KfRCCOHipNALIYSLk0IvhBAuTgq9EEK4OKUdNHraDUMo\nlQwcNTsHEASkmB2iEJLr1kiuW+es2STXjdXQWt90VEinKPTOQim1SWvdwuwc15Jct0Zy3TpnzSa5\nbEOaboQQwsVJoRdCCBcnhf5qk80OcB2S69ZIrlvnrNkklw1IG70QQrg4OaIXQggXV6YKvVJqilLq\ntFJqZ4FlTZRSfyql4pRS85RSFQs89qJS6qBSap9Sqqcdc1VTSi1XSu1RSu1SSo2xLg9QSi1RSh2w\nXle2LldKqfet2XYopZo5ONcg632LUqrFNc+x+2t2g1yTlFJ7ra/Jz0opfyfJ9W9rpm1Kqd+UUmHW\n5aa+jwUeH6eU0kqpIGfIpZSaoJQ6YX29timl+hR4jmnvo/Wxp6373qWUesuRuUpEa11mLkAnoBmw\ns8CyjUBn6+2RwL+tt+sD2wFvIAqIB9ztlCsUaGa9XQHYb93/W8B46/LxwJvW232AhRgTv7QB1js4\nVz2gDrACaFFgfYe8ZjfI1QPwsC5/s8DrZXauigXWGQ186gzvo/V+NWAxxnksQc6QC5gAjCtkfbPf\nx67AUsDb+liII3OV5FKmjui11iuBtGsW1wFWWm8vAe623h4ATNdaZ2utDwMHgVZ2ypWotd5ivX0e\n2AOEWzNMs642DbizQLavtWEd4K+sE7I7IpfWeo/Wel8hT3HIa3aDXL9prfOsq60DIpwk17kCq/kB\nl34YM/V9tD78X+D5ApmcJVdhTH0fgceBN7TW2dbHTjsyV0mUqUJ/HTuB/tbbgzCOcMB4Y48XWC+B\nG/8jtAmlVCQQC6wHqmitE8H4xweEmJXtmlzX40y5RmIclTpFLqXUa0qp48BDwD+dIZdSqj9wQmu9\n/ZrVTH+9gKeszUZTLjVZOkGu2kBHpdR6pdQfSqmWZuW6VVLojYLwpFJqM8bXtBzr8sLmw7VrFyWl\nVHngJ2DsNUeBf1m1kGV2y1bacimlXgbygO+cJZfW+mWtdTVrpqfMzoXx+rzMlQ+dq1Y1K5f19foE\nqAU0BRKBd5wklwdQGaM56zlghlJKOTpXcZT5Qq+13qu17qG1bg78gNG+BsancrUCq0YAJ+2VQynl\nifGP6jut9Wzr4qRLX5mt15e+Kjos23VyXY/puZRSw4B+wEPa2oDqDLkK+J4rzYNm5qqF0Z68XSl1\nxLrvLUqpqibnQmudpLXO11pbgM+50gxi9vuYAMy2NmltACwYY944tFYUi9k/Ejj6AkRy9Y+xl35Q\ncQO+BkZa7zfg6h9YDmG/H2OVdd/vXbN8Elf/GPuW9XZfrv6xbIMjcxV4fAVX/xjrkNfsBq9XL2A3\nEHzNcrNzxRS4/TQwy5neR+s6R7jyY6ypuYDQArefwWj/dob38THgX9bbtTGaa5Qja0Wx/yazAzj0\njzWO2BOBXIxP4VHAGIxf1fcDb2A9icy6/ssYR/j7gN52zNUB46veDmCb9dIHCASWAQes1wEF/iF+\nZM0WV7DYOijXQOvrlw0kAYsd+ZrdINdB63++S8s+dZJcP2H8FrQDmIfxA63p7+M16xzhSqE3+9/X\nN9b97gDmcnXhN/N99AK+tb6XW4DbHJmrJBc5M1YIIVxcmW+jF0IIVyeFXgghXJwUeiGEcHFS6IUQ\nwsVJoRdCCBcnhV4IIVycFHohhHBxUuiFEMLF/T9gstQF06wI9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e8ba2ee80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "coeffs = np.array(coeffs)\n",
    "x = [185+ i for i in range(0, 81, 8)]\n",
    "plt.plot(x, coeffs[:,0], label = 'corn')\n",
    "plt.plot(x, coeffs[:,1], label = 'soybean')\n",
    "# plt.plot(x, coeffs[:,2], label = 'grass')\n",
    "# plt.plot(x, coeffs[:,3], label = 'biase')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.149317318397 0.00636143446433 0.290937495891\n"
     ]
    },
    {
     "data": {
      "image/png": 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29DfYcykoa+CW9flMTIrhtVsXMCbBv/vg98eq7HQKyhrYXOTZBsAthyrpMGgj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\nEE36SikVQDTpK6VUANGkr5RSAeT/A9+3bQDAbHMqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e8437ae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(x, rsquares, label = 'r-squared')\n",
    "plt.legend()\n",
    "print(np.mean(rsquares), np.min(rsquares), np.max(rsquares))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1552, 6275) (1552, 6279)\n",
      "(1557, 6279) (1557, 6277)\n",
      "fuckit\n",
      "(array([  85.7710763 ,   81.01829318,  106.00607548,   66.74423031]), array([ 778.96294232]), 4, array([ 2.42635342,  1.18245225,  0.56001946,  0.34958254]))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bowen\\Desktop\\work\\Satellite_Big_Data_Scripts\\fpar_workflow.py:56: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
      "  assert self.cdl_matrix != None and self.fpar_vals != None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1552, 6275) (1552, 6279)\n",
      "(1557, 6279) (1557, 6277)\n",
      "fuckit\n",
      "(array([  89.30411247,   78.60330194,  102.1231668 ,   67.30180423]), array([ 464.24816004]), 4, array([ 2.49568581,  1.21623186,  0.57010408,  0.3740793 ]))\n",
      "(1552, 6275) (1552, 6279)\n",
      "(1557, 6279) (1557, 6277)\n",
      "fuckit\n",
      "(array([ 82.04338882,  88.34563677,  79.58461923,  64.04032245]), array([ 117.52432286]), 4, array([ 2.44911802,  1.17760732,  0.56252109,  0.34517441]))\n",
      "(1552, 6275) (1552, 6279)\n",
      "(1557, 6279) (1557, 6277)\n",
      "fuckit\n",
      "(array([ 81.39812755,  88.68257435,  91.14383367,  79.73398508]), array([ 285.18097055]), 4, array([ 2.38368166,  1.17558974,  0.47648288,  0.32765116]))\n",
      "(1552, 6275) (1552, 6279)\n",
      "(1557, 6279) (1557, 6277)\n",
      "fuckit\n",
      "(array([ 87.4160618 ,  84.32978023,  66.50022316,  94.96046738]), array([ 216.38210453]), 4, array([ 2.22373496,  1.11556043,  0.54919563,  0.31141946]))\n",
      "(1552, 6275) (1552, 6279)\n",
      "(1557, 6279) (1557, 6277)\n",
      "fuckit\n",
      "(array([  75.13822755,  105.20854746,   67.49428348,   66.27659868]), array([ 26.42136334]), 4, array([ 1.37097414,  0.5979097 ,  0.34419697,  0.10497449]))\n",
      "(1552, 6275) (1552, 6279)\n",
      "(1557, 6279) (1557, 6277)\n",
      "fuckit\n",
      "(array([  74.75156237,   92.84124116,  100.61650772,   68.78181677]), array([ 230.1134436]), 4, array([ 2.47152008,  1.20119972,  0.49300734,  0.33782028]))\n",
      "(1552, 6275) (1552, 6279)\n",
      "(1557, 6279) (1557, 6277)\n",
      "fuckit\n",
      "(array([  70.03038166,   76.53531188,  110.109586  ,   63.14785939]), array([ 271.24696318]), 4, array([ 2.38571905,  0.84358412,  0.54767593,  0.37140298]))\n",
      "(1552, 6275) (1552, 6279)\n",
      "(1557, 6279) (1557, 6277)\n",
      "fuckit\n",
      "(array([ 65.16445911,  66.4858793 ,  83.78082729,  73.89957271]), array([ 333.64466275]), 4, array([ 2.49568581,  1.21623186,  0.57010408,  0.3740793 ]))\n",
      "(1552, 6275) (1552, 6279)\n",
      "(1557, 6279) (1557, 6277)\n",
      "fuckit\n",
      "(array([ 41.32408858,  45.43486829,  91.12279646,  54.6079745 ]), array([ 391.17662534]), 4, array([ 2.35244402,  1.18212039,  0.56685163,  0.37264412]))\n",
      "(1552, 6275) (1552, 6279)\n",
      "(1557, 6279) (1557, 6277)\n",
      "fuckit\n",
      "(array([ 36.89568119,  18.37306427,  81.68819869,  60.04670861]), array([ 556.63210742]), 4, array([ 2.49568581,  1.21623186,  0.57010408,  0.3740793 ]))\n"
     ]
    }
   ],
   "source": [
    "coeffs = []\n",
    "rsquares = []\n",
    "for i in range(185, 266, 8):\n",
    "    prefix ='FPAR_A2016'\n",
    "    suffix = '.hdf'\n",
    "    query = prefix + str(i) + suffix\n",
    "    fp_workflow.continue_workflow(query, p1, p2, p3, p4)\n",
    "    coeffs.append(fp_workflow.reg_coeffs)\n",
    "    y = fp_workflow.fpar_vals\n",
    "    x = fp_workflow.cdl_matrix\n",
    "    yx = np.append(x, np.array(y).reshape(-1,1), axis = 1)\n",
    "    \n",
    "    np.random.shuffle(yx)\n",
    "    sampleyx = yx\n",
    "#     sampleyx = yx[np.random.choice(yx.shape[0], 100, replace=False)]\n",
    "    y_ = sampleyx[:,-1]\n",
    "    x_ = sampleyx[:,:-1]\n",
    "    r_squared = sm.OLS(y_, x_).fit().rsquared\n",
    "    rsquares.append(r_squared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   44.,    31.,    29.,    71.,    72.,   104.,   177.,   370.,\n",
       "         1247.,  4049.]),\n",
       " array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#matplotlib inline\n",
    "plt.hist(np.sum(fp_workflow.cdl_matrix[:,0:2], axis = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1652, 6695) (1652, 6709)\n",
      "(1662, 6709) (1662, 6695)\n",
      "fuckit\n",
      "(array([  83.90493839,   76.36140545,    0.        ,  101.31082992]), array([], dtype=float64), 3, array([ 5.92926643,  3.54830834,  0.39245072,  0.        ]))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bowen\\Desktop\\work\\Satellite_Big_Data_Scripts\\fpar_workflow.py:55: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
      "  assert self.cdl_matrix != None and self.fpar_vals != None\n"
     ]
    }
   ],
   "source": [
    "fp_workflow.continue_workflow('FPAR_A2016185.hdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x76e6b18470>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZ9BLUuUMekmq3P8B7+4JVkpi1rMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x76d350ebe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.scatter(fp_workflow.cdl_matrix[:,3], fp_workflow.fpar_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0037305148434825e-12"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(fp_workflow.cdl_matrix@fp_workflow.reg_coeffs - fp_workflow.fpar_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fp_workflow.fpar_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1.0, copy_X=True, fit_intercept=False, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "clf = Ridge(alpha = 1.0, fit_intercept = False)\n",
    "clf.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 85.10776907  80.00577495  69.80132729  67.5235043 ] [ 85.0903471   79.98963568  71.59279432  67.71203231]\n"
     ]
    }
   ],
   "source": [
    "print(clf.coef_, fp_workflow.reg_coeffs)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'')"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PvPcwqo988+m9ocsr/viMJIwM10Jz8ndiL4nSK/rNG1ZSTbBAPjVd51vPhEfS\nXnPB8shf/KHBRXOMWt4ve5wEytzpcpjcrkWpN2YQiW/AkeEak7//8XneKWu/+ihf2v4cUy13v4vO\nWca2HXvmjWLiptJRuqlWHZhXRi+1rMe2HXtSr8cGzUDG147w1JaL+frGNYlH9979Nm9YGTjrG4BM\ns8GoK2vVSmzfy0J7XYXN6Lydldrxlhiiro2quzgax7Qjyzbee5hk9ldvzPB7Dz7P+q2P86XtzyVa\nZqxWZHZXqrAku0UnZoQ+UPTja0fYdtXqRAa0qGW1sTOWctsV4VFvfv9g/y+7i7LyT9HG146w8fz5\nM4sko7rpQw2+tnFN5IigfVo4MTnF5vt3zRllTtcboaOYuKn0dMRotd44Nl+7tX1OO20Nm4F4tI/u\n/Tt8+fcoDbrf+NoRtl25ek7E8XCtytc2rmHblasj5YqKUm434C8Zmru7UdodiuJon01C9Iwuamkm\n7trZggYQ9452KifQvun6bBlcOdQ4FjuKb4/oXTJUZeOvLo/dTa4TxS69MRay+wj72bZjD09tuTjU\nv9mbmgbtQhNH+7Q2yCCWJHGWl9ogyoWv/ZnbduxxyljojeDiZPHuH/Tsisi8Z7XvJBRlFA67r2vU\nZXsitCREXRsXrZs04VrQc/NcA26fTbY/q52wNmlfJgu6NsxA6bVnXhuUZ0mc5pVjfO1IbnEMYW3r\n4mjQibQYpR/Rt5PVIOeNWuKMlWlGou8cPjpnaSTsHl5QVhSecTXKMOQZbf0kkXtqus7gomhJ3jl8\nlBXvrc2Tt1athP5AxBlQvXq+6JxlgffNsiG8V18rtjzCWTd8hxUhBrf28/3tFtc3khi6w57pasB1\nxXPHXfvVRwONoH6C5K9WZF7/DZI7TGm6Gmpdln285b8077rLElYawtrWpczXXBBuM8yLvlP0no9w\n2t9I/6991PQ0jcHOS6rlLY2EZVF0CqBR2PnqgUjDkGe09b+USeWuN44xQLjtYLre4KmXDsyRN87g\nFWZA9dczLdmD7pt2lN6+3Ob9EIX5M6dNqOZq6I6SMWvitXZOqlUDl+w237drXrnb5ff7xof5f3ty\nh+FiqB0Zrs1bMqtVB6i0dz4JljPsnRchtB2C2mrxYDLF722CEkRUmSsizgnSslL6yFiYn+vm0JGj\nqV6UsARg/hwlIs116eGhKj9/9+icaWp1IFmiLxFYlCE5mOsST3siqM3370q14cRwrcrb7zac1hS9\nvB5BqYRdlF6W5Y+k9wy7d9j5XuRpEb7PRUT31qoVTqwOhL4T/vIEpXYOW25xibT2nu/9eIdF5Ib1\ni7D7elHRfnnDuuVwrcpzN30POrtAAAAbzElEQVQ85Nv5JIkcjkpgF3avPBObdSSpWS8QlOsmLXFZ\nHv0d/uChBtWKNJVfvTEbzpwk0ZcqMNDsiNP1RuJQeFfjVbsRGNx3a/KTZH3VO9fLQJk05DvOIJiG\nuGvbv49aWgvaPSwPkpZvqDrA4RmdF2hXqw7wbuPYbJ1H5W73yuPlf/H6e1z/cPXLb3+vvHVxb6DS\nnoQt7Bl+Dh5qzNv3NYy3E9oFPDlc3uWgBHZB90rzDuRJ6RV9GqNoECMtw2aSezdmlMUnLJozWkhq\n3PHusfiE5DtAJTHa+nHdrSkrnmE7Tad2MQjmdc+we0edX1REY9Lo3sNHNbAPLF18wpzZSVy/TLOL\nmmukdftSSZI6i6oPV3nT9BlXQ62Lh1QWp4C8KP0afR5RZWGGMpd7t5+TxlA4NV1PrGhr1Qrr3r8k\n1hbhYgRMGovgSpq2mZicYs0tjwbWR9AOUlERjO1EGd7a/fvjzofg8iWVKYmMQbgYvL37xhk6kyh5\n4XjaXxcDdVqirneVN60McW2RR/k6RekVfdiv9XCt6rSrT5ShzGUkEDRaziMpVhReIrAf7H07cqkn\nkfEypZ6viIQar5KOpLy0tmFLRP6yhBlKoxSrZ3gL9HUPKL93flj/aS9fGpnCnuk3DobttgThAWtB\n/bLd0NmOq5uff4nR1UCdlqj3yWVsEmUodXl2u1Ha24kur/J1itSKXkRWishzvv/+SUS+JCJLReS7\nIvJi6++SPAVuJ2g0Wh0Qbr78XDZvWMlpw7VId8Wp6Tq/9+DzgS9ju2tiO2G/6Ddffm7g6GbTutHU\nrly1aoWvb1zD1zeuAZph2nFLVmH+0+2E+dYvGarGjmhuv3o1//uvr0rljhcoR4Rh2l+WoGU1f/Rm\nGONrRxganL9i6fn3B51/+9WrnUarYTJ9+d5dnLnlEdZ+9VHW3PJobJ140b0/3XoZmzes5N2IyMna\nooFY2bxZxvXbn2NocFFgP/Qic6NG/bVqc8vN9hbyL2N5cqddsgsi7H06YVG0+qpVK7Ob5KRlfO3I\nrB6ZPtRApOnBtG+6zrYdezqSeTIPUit6Vd2jqmtUdQ1wHnAI+CtgC/CYqp4NPNb6XCwB0Zftroca\ncJrHocYxfvfe5+a5i8VtTBD2ix7mXpcm8rHd5TBpno00y08e04cac8oRFM3prT8mdcdLI6uL8S9L\necOOu7pLRhlvlbn71rrUiTdDiHLKqjeOJdo7d2q6zgPPTs1LFX3bFasYO2NpqDeAd06YYbPIxFxh\n9f9uWE4B8htxR+093Mk0w1nJyxh7CfCSqr4qIp8GLmwdvwt4EvhKTs+ZR9BotDGjgYaaqGWOYzo3\n5W2cITbIeOsnzACTJPIxaG/YrNG4YeeEGT5dDUnt0cntHhsuxsskxtIsxto017rUQ1JDalyduDga\nxLVR2CyjPVU0NNstLKrVOzcuYrwogsqYdj/hJMS1QafSDGclrzX6zwL3tP59iqq+AdD6e3LQBSJy\nnYjsFJGd+/e758ZuJ2oUleVeUSOUKCOMqzHOxejmj5hLsr+ln3cOH01lpMxiaHIZMYdFnIYZhSsD\n6dMst5P22naZ29PZXnTOssRLc1OtiNWgZ8W1d3vq6SBcZy83ToTPFF0jmTtNXrJEvbNZZoi9RGZF\nLyKDwOXAfUmuU9U7VXVMVceWLYteC48ibCSRJn+EP1I16r5hU8Ikxrj26WitOjBrXGqPmIuLOoxi\nut5wNlLmZUiLSmML0RGnQYneANoD+7LInObaIJnbE8G1L4m49sGkUabgvgNSXFvA8V2dXO6Rd1/J\nQh6yxL2zaRwyepHMkbGtpZrfVtWPtz7vAS5U1TdE5FTgSVWN/InNEhkbFnn2mfNG2P691xJFnfoj\n6NJEtLlEc7ZH8boET0Qt87gGWbVPZ9PI4Upc3UXVE4QHvSWZkreX76JzlvHET/aH7qEaVxeusQbt\nbe0aYekFDcX5bieNqnTpx2fd8J3QGXDaKM4i+ldYlHqW+8e9s3Ft6I/67UZQVCcjY6/h+LINwMPA\ntcDW1t+HcnhGKGGRZwDbv58sv7Xf0JQmoi1umhwUxesSYRk1NXT9GWtfNkkjhytxdZfWkOo6RQ4q\nn3/E6i8v4FQXrs8OikK++eEXYqOKvefG/SgkVbou/ThqmTOtks+7f0VFqWe5v0tKZjhef15aCH80\nPLj1oW6SSdGLyBDwMeB/9B3eCtwrIp8H9gJXZXmGC0GGmvVbH0+czyUugtTl+qSpjbMYKZOkavWX\nLa0cSYiqu7h6CiuP6xTZxYjpd8V0qQtXQ2ug//qOPbGKviISK3OcA0AYcf04LMK6IpLqeUX0r6KM\nollSMnsEOUn0mpE20xq9qh5S1feq6tu+Y2+p6iWqenbr74HsYrqTdj/HvKL4goxxnkE07Ug2bkef\nOAOg6x63nTIqxe5QFGCQdTE8eiQZfbvWRVDK5HbSRlhHpXRuv3fWyNsg1r0/ONQlbfrcbuQpSnv/\nPAy63X6fXCh9ZKyftPs5RhlYkxAWeekZRMPSEseNVKOMTkHfbVo3GmmgcjHQFUlcedp3DHM1PHq4\nliMqdW77vrZBKZPXn7XUyRAYJY93XVRsRVAcRV5+3BOTU/xg79vzjq8/a2nq9LlF9K+ijKJ5GHS7\n/T650Fdpil3c0VDmGGjzTBnqETajGK5VOXz0mLOBN8ygldXQFWXADjNYloWJySmnNfGo1LntbZI1\nZbKLQdTlnDxTN8e9M1nv2e4k4H2OylTpv0dQn3cxihbZX5PI1gl5YAGnKQ7D62BQvHU8bMr2dr3B\nHRvXOD0/zKDVnko2jeEnyEB30TnLMt+324T1hSVDVS77lVMjf8Si2iTr1NzFIOpyTl5LBC7vTNZ7\nepHo/r8Q3a9cjLh5e92kLV+UbL04SCr9iN5lPT7PSDkX8hh5RW16EbSem7WMRWz00WmKKkOv1E1e\nchTxzuTRX3ulnoPoVdkWzIjexdCVxsiaZXlk84aVgVO5PAw8rmlpkxJ2fRF56osi7Yg3rq3zaM88\nyEuOIt6ZsH6SpL9G9cEztzyS6D10fX9dz8tzNtWNkX/pjbEuhq60fsBpjV5FGnhcU+YmJex6gVIk\nbYJ0RjGXtu6VaNC85Mj7nZmYnAr1SErSX6PkSvIeur6/Sd7zPAyueaSxTkvpl26C9kCtVoTzVyzh\n6ZcPMqNKRYRrLlju7EXQC9O0KINp+/ZpaQw/QZGjdz+9NzAAq9vTU1e6Fc3crVFaWvI2HobVoQCf\nWzfq3F+T2Nva+6O/DQYcl4uSvOd51FkRemXBLN0A88JDj84oT7103H1/RnU2MtJF2feCX2yUgWfs\njKW5et14eVrCfvJ7yR84im5EMxcdZVwEeRsPw+pQab5vrv21Xa6w/tiuLNvbwHW5KMl7nkeddVOv\nlF7RB21WEdZB7nnmNSdFX8R+pWkIi8hLGrHbTljkYpjhrJf8gePodDRzJ6KMiyBrH/ITF7md5Fn+\nc8Ny8LQvB7nuG+26J3BYf89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VrTZ+QUS+1WkZ88ShP4+29rmYbPXpS7shZx6IyJ+LyJsi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\nY4reMAyjzzFFbxiG0ef8/8BVsOia6BUoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e84f7d2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HgX8ODInIHGdV/y7gUP5uGr2Gd0u64coL2fDQvqYkVi5TqizZuI2RoQrrli1M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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e844e0400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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K2HJJWHflOQDTxruVSzKVuVL/x+ZXBetWyfp9YIPG1MWlvvJxeGTUt0VCSYQj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e8449bb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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x0TmrfOfYr4yzM/dh72vQ4sd7PKvlXKTlHfUe9jMWjC0YZ6k7v9CgOiKMj1aZ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\nMEqOKXrDMIySY4reMAyj5Px/nvAbtPW4lyIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e82ca5c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "x = fp_workflow.cdl_matrix\n",
    "y = fp_workflow.fpar_vals\n",
    "plt.scatter(x[:,0], y)\n",
    "plt.title('corn')\n",
    "plt.figure()\n",
    "plt.scatter(x[:,1], y)\n",
    "plt.title('soybeans')\n",
    "plt.figure()\n",
    "plt.scatter(x[:,2], y)\n",
    "plt.title('grass')\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(x[:,3], y)\n",
    "plt.title('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 992.,  554.,  606.,  676.,  644.,  607.,  576.,  518.,  486.,  535.]),\n",
       " array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qcs = []\n",
    "for i in fp_workflow.fp_ptrs:\n",
    "    qcs.append(fp_workflow.fpar_.qc[i[0], i[1]])\n",
    "    \n",
    "# good_qc(qcs)\n",
    "plt.hist(fp_workflow.cdl_matrix[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   44.,    31.,    29.,    71.,    72.,   104.,   177.,   370.,\n",
       "         1247.,  4049.]),\n",
       " array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plt.scatter(np.arange(47), y)\n",
    "plt.hist(np.sum(fp_workflow.cdl_matrix[:,0:2], axis = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.00000000e+00,   0.00000000e+00,   1.11022302e-16, ...,\n",
       "         0.00000000e+00,   0.00000000e+00,   0.00000000e+00])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp_workflow.cdl_matrix[:,3] - 1 +  fp_workflow.cdl_matrix[:,2] + fp_workflow.cdl_matrix[:,1] + fp_workflow.cdl_matrix[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[185, 193, 201, 209, 217, 225, 233, 241, 249, 257, 265]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.40522876,  0.44117647,  0.04901961,  0.10457516],\n",
       "       [ 0.00617284,  0.        ,  0.        ,  0.99382716],\n",
       "       [ 0.33986928,  0.66013072,  0.        ,  0.        ]])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp_workflow.cdl_matrix[np.random.choice(fp_workflow.cdl_matrix.shape[0], 3, replace=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [1],\n",
       "       [1]])"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1,1,1])\n",
    "a.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "3892\n",
      "3906\n"
     ]
    }
   ],
   "source": [
    "bad = []\n",
    "for i in range(len(y)):\n",
    "    if x[i][0] >= 0.9 and y[i] <= 60:\n",
    "        print(i)\n",
    "        bad.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bad_points = [fp_workflow.fp_ptrs[i] for i in bad]\n",
    "# qcs = [fp_workflow.fpar_.qc[]]\n",
    "qcs = []\n",
    "for i in bad_points:\n",
    "    qcs.append(fp_workflow.fpar_.qc[i[0], i[1]])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good_qc(qcs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 10145)"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proc = fpar_utils()\n",
    "proc.coords_to_ind(50, -69.2724108461701)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-90.313070850359935, 41.52256760817977),\n",
       " (-90.3076311761086, 41.544318559782923),\n",
       " (-90.3076311761086, 41.533443083981346),\n",
       " (-90.302191501857251, 41.544318559782923),\n",
       " (-90.302191501857251, 41.533443083981346),\n",
       " (-90.296751827605902, 41.533443083981346),\n",
       " (-90.247794759343833, 41.544318559782923),\n",
       " (-90.144440948568331, 41.576944987187659),\n",
       " (-90.139001274316996, 41.576944987187659),\n",
       " (-90.128121925814312, 41.511692132378187),\n",
       " (-90.095483880306261, 41.549756297683714),\n",
       " (-90.073725183300894, 41.528005346080555),\n",
       " (-90.019328440787476, 41.511692132378187),\n",
       " (-89.93229365276602, 41.528005346080555),\n",
       " (-89.905095281509304, 41.538880821882131),\n",
       " (-89.899655607257955, 41.538880821882131),\n",
       " (-89.89421593300662, 41.571507249286867),\n",
       " (-89.89421593300662, 41.549756297683714),\n",
       " (-89.89421593300662, 41.544318559782923),\n",
       " (-89.89421593300662, 41.538880821882131),\n",
       " (-89.888776258755286, 41.571507249286867),\n",
       " (-89.872457236001253, 41.582382725088443),\n",
       " (-89.872457236001253, 41.571507249286867),\n",
       " (-89.867017561749918, 41.576944987187659),\n",
       " (-89.861577887498569, 41.582382725088443),\n",
       " (-89.861577887498569, 41.576944987187659),\n",
       " (-89.85613821324722, 41.576944987187659),\n",
       " (-89.85613821324722, 41.506254394477402),\n",
       " (-89.845258864744551, 41.576944987187659),\n",
       " (-89.845258864744551, 41.549756297683714),\n",
       " (-89.839819190493202, 41.576944987187659),\n",
       " (-89.828939841990518, 41.582382725088443),\n",
       " (-89.812620819236486, 41.582382725088443),\n",
       " (-89.796301796482481, 41.533443083981346),\n",
       " (-89.790862122231118, 41.533443083981346),\n",
       " (-89.779982773728449, 41.549756297683714),\n",
       " (-89.779982773728449, 41.50081665657661),\n",
       " (-89.763663750974416, 41.566069511386075),\n",
       " (-89.758224076723081, 41.576944987187659),\n",
       " (-89.741905053969049, 41.582382725088443),\n",
       " (-89.736465379717714, 41.582382725088443),\n",
       " (-89.72558603121503, 41.506254394477402),\n",
       " (-89.720146356963681, 41.506254394477402),\n",
       " (-89.709267008460998, 41.576944987187659),\n",
       " (-89.703827334209649, 41.576944987187659),\n",
       " (-89.698387659958314, 41.571507249286867),\n",
       " (-89.692947985706979, 41.571507249286867),\n",
       " (-89.692947985706979, 41.52256760817977),\n",
       " (-89.665749614450277, 41.560631773485291),\n",
       " (-89.627671894690877, 41.52256760817977),\n",
       " (-89.584154500680143, 41.576944987187659),\n",
       " (-89.578714826428808, 41.582382725088443),\n",
       " (-89.529757758166724, 41.582382725088443),\n",
       " (-89.529757758166724, 41.506254394477402),\n",
       " (-89.529757758166724, 41.50081665657661),\n",
       " (-89.524318083915375, 41.506254394477402),\n",
       " (-89.491680038407338, 41.50081665657661),\n",
       " (-89.317610462364399, 41.50081665657661),\n",
       " (-89.225136000091581, 41.528005346080555),\n",
       " (-89.197937628834879, 41.576944987187659),\n",
       " (-89.197937628834879, 41.506254394477402),\n",
       " (-89.165299583326828, 41.571507249286867),\n",
       " (-89.165299583326828, 41.566069511386075),\n",
       " (-89.165299583326828, 41.555194035584499),\n",
       " (-89.154420234824144, 41.566069511386075),\n",
       " (-89.154420234824144, 41.506254394477402),\n",
       " (-89.14354088632146, 41.511692132378187),\n",
       " (-89.094583818059391, 41.549756297683714),\n",
       " (-89.094583818059391, 41.533443083981346),\n",
       " (-89.045626749797307, 41.576944987187659),\n",
       " (-89.002109355786587, 41.566069511386075),\n",
       " (-88.996669681535238, 41.560631773485291),\n",
       " (-88.991230007283889, 41.560631773485291),\n",
       " (-88.985790333032554, 41.560631773485291),\n",
       " (-88.964031636027187, 41.506254394477402),\n",
       " (-88.953152287524489, 41.511692132378187),\n",
       " (-88.942272939021819, 41.506254394477402),\n",
       " (-88.925953916267787, 41.528005346080555),\n",
       " (-88.909634893513754, 41.560631773485291),\n",
       " (-88.893315870759736, 41.560631773485291),\n",
       " (-88.833479453994983, 41.582382725088443),\n",
       " (-88.822600105492285, 41.506254394477402),\n",
       " (-88.795401734235583, 41.571507249286867),\n",
       " (-88.795401734235583, 41.566069511386075),\n",
       " (-88.795401734235583, 41.560631773485291),\n",
       " (-88.789962059984248, 41.571507249286867),\n",
       " (-88.789962059984248, 41.566069511386075),\n",
       " (-88.789962059984248, 41.549756297683714),\n",
       " (-88.784522385732913, 41.566069511386075),\n",
       " (-88.735565317470829, 41.517129870278978),\n",
       " (-88.73012564321948, 41.52256760817977)]"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([1559, 6277]),\n",
       " array([1555, 6278]),\n",
       " array([1557, 6278]),\n",
       " array([1555, 6279]),\n",
       " array([1557, 6279]),\n",
       " array([1557, 6280]),\n",
       " array([1555, 6289]),\n",
       " array([1549, 6308]),\n",
       " array([1549, 6309]),\n",
       " array([1561, 6311]),\n",
       " array([1554, 6317]),\n",
       " array([1558, 6321]),\n",
       " array([1561, 6331]),\n",
       " array([1558, 6347]),\n",
       " array([1556, 6352]),\n",
       " array([1556, 6353]),\n",
       " array([1550, 6354]),\n",
       " array([1554, 6354]),\n",
       " array([1555, 6354]),\n",
       " array([1556, 6354]),\n",
       " array([1550, 6355]),\n",
       " array([1548, 6358]),\n",
       " array([1550, 6358]),\n",
       " array([1549, 6359]),\n",
       " array([1548, 6360]),\n",
       " array([1549, 6360]),\n",
       " array([1549, 6361]),\n",
       " array([1562, 6361]),\n",
       " array([1549, 6363]),\n",
       " array([1554, 6363]),\n",
       " array([1549, 6364]),\n",
       " array([1548, 6366]),\n",
       " array([1548, 6369]),\n",
       " array([1557, 6372]),\n",
       " array([1557, 6373]),\n",
       " array([1554, 6375]),\n",
       " array([1563, 6375]),\n",
       " array([1551, 6378]),\n",
       " array([1549, 6379]),\n",
       " array([1548, 6382]),\n",
       " array([1548, 6383]),\n",
       " array([1562, 6385]),\n",
       " array([1562, 6386]),\n",
       " array([1549, 6388]),\n",
       " array([1549, 6389]),\n",
       " array([1550, 6390]),\n",
       " array([1550, 6391]),\n",
       " array([1559, 6391]),\n",
       " array([1552, 6396]),\n",
       " array([1559, 6403]),\n",
       " array([1549, 6411]),\n",
       " array([1548, 6412]),\n",
       " array([1548, 6421]),\n",
       " array([1562, 6421]),\n",
       " array([1563, 6421]),\n",
       " array([1562, 6422]),\n",
       " array([1563, 6428]),\n",
       " array([1563, 6460]),\n",
       " array([1558, 6477]),\n",
       " array([1549, 6482]),\n",
       " array([1562, 6482]),\n",
       " array([1550, 6488]),\n",
       " array([1551, 6488]),\n",
       " array([1553, 6488]),\n",
       " array([1551, 6490]),\n",
       " array([1562, 6490]),\n",
       " array([1561, 6492]),\n",
       " array([1554, 6501]),\n",
       " array([1557, 6501]),\n",
       " array([1549, 6510]),\n",
       " array([1551, 6518]),\n",
       " array([1552, 6519]),\n",
       " array([1552, 6520]),\n",
       " array([1552, 6521]),\n",
       " array([1562, 6525]),\n",
       " array([1561, 6527]),\n",
       " array([1562, 6529]),\n",
       " array([1558, 6532]),\n",
       " array([1552, 6535]),\n",
       " array([1552, 6538]),\n",
       " array([1548, 6549]),\n",
       " array([1562, 6551]),\n",
       " array([1550, 6556]),\n",
       " array([1551, 6556]),\n",
       " array([1552, 6556]),\n",
       " array([1550, 6557]),\n",
       " array([1551, 6557]),\n",
       " array([1554, 6557]),\n",
       " array([1551, 6558]),\n",
       " array([1560, 6567]),\n",
       " array([1559, 6568])]"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-88.78452238573291, -88.77908271148156, 41.56063177348529, 41.566069511386075]"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get_cdl_box_data(self, min_lon, max_lon, min_lat, max_lat)\n",
    "fp_workflow.fpar_.get_fpar_bound(1551, 6558)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 5, 5],\n",
       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 5, 5],\n",
       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=uint8)"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp_workflow.cdl_.get_cdl_box_data(-88.78452238573291, -88.77908271148156, 41.56063177348529,41.566069511386075)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.98039216,  0.01960784,  0.        ,  0.        ])"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp_workflow.cdl_matrix[3764]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
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 },
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