dharma_single.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib.cm as cm\n",
      "import matplotlib.colors as colors\n",
      "from matplotlib import dates\n",
      "from matplotlib.colors import LogNorm\n",
      "from netCDF4 import num2date,date2num,Dataset\n",
      "import matplotlib._pylab_helpers\n",
      "#from joblib import Parallel, delayed\n",
      "import scipy.interpolate as interpolate\n",
      "import scipy.stats as stats\n",
      "from scipy.stats import pearsonr\n",
      "from scipy.stats.mstats import scoreatpercentile\n",
      "import pyart\n",
      "import os,subprocess,time,copy\n",
      "import scipy\n",
      "import math\n",
      "import matplotlib.gridspec as gridspec\n",
      "import cPickle as pickle\n",
      "#import pickle\n",
      "import pprint\n",
      "import pyproj\n",
      "import scipy.interpolate as interpolate\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filnm = '/home/mvanlier/mcmc_stick/single_runs/sgp_test_oi_00.nc'\n",
      "nc = Dataset(filnm,'r')\n",
      "vd = nc.variables['vd'][:]\n",
      "zc = nc.variables['zc'][:]\n",
      "d_bin = nc.variables['d_bin'][:]\n",
      "m_bin = nc.variables['m_bin'][:]\n",
      "params = nc.variables['parameters'][:]\n",
      "fwd_obs = nc.variables['fwd_obs'][:,:]\n",
      "fwd_obs_alt = nc.variables['fwd_obs_alt'][:,:]\n",
      "obs = nc.variables['obs'][:,:].T\n",
      "ice_bins = nc.variables['ice_bins'][:,:]\n",
      "rain_bins = nc.variables['rain_bins'][:,:]\n",
      "#..\n",
      "print fwd_obs.shape\n",
      "print obs.shape\n",
      "print ice_bins.shape\n",
      "#..\n",
      "nc.close()\n",
      "#.."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(256, 67)\n",
        "(256, 67)\n",
        "(50, 67)\n"
       ]
      }
     ],
     "prompt_number": 137
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print params"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.001       0.94999999]\n"
       ]
      }
     ],
     "prompt_number": 138
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vdmin = -5.\n",
      "vdmax = vd.max()\n",
      "zmin = 0.\n",
      "zmax = zc.max()\n",
      "dbzmax = 50.\n",
      "dbzmin = -20.\n",
      "#.."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 139
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = figure(figsize=(15,7))\n",
      "#..\n",
      "ax1 = fig.add_subplot(131)\n",
      "ax1.pcolormesh(vd,zc,obs.T,vmin=dbzmin,vmax=dbzmax,cmap=cm.cubehelix_r)\n",
      "ax1.set_xlim(vdmin,vdmax)\n",
      "ax1.set_ylim(zmin,zmax)\n",
      "#..\n",
      "ax2 = fig.add_subplot(132)\n",
      "ax2.pcolormesh(vd,zc,fwd_obs.T,vmin=dbzmin,vmax=dbzmax,cmap=cm.cubehelix_r)\n",
      "ax2.set_xlim(vdmin,vdmax)\n",
      "ax2.set_ylim(zmin,zmax)\n",
      "#..\n",
      "ax3 = fig.add_subplot(133)\n",
      "ax3.pcolormesh(vd,zc,fwd_obs_alt.T,vmin=dbzmin,vmax=dbzmax,cmap=cm.cubehelix_r)\n",
      "ax3.set_xlim(vdmin,vdmax)\n",
      "ax3.set_ylim(zmin,zmax)\n",
      "#.."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 140,
       "text": [
        "(0.0, 12396.6)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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FVhdjQmQoEsJqnZDUh8aFmHLZWuzQWo6pmOnqBegvAFBVq1R14mUVymEyHFya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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fa6377727d0>"
       ]
      }
     ],
     "prompt_number": 140
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#..Okay we're learning diddly squat from this, lets look at bins\n",
      "fig = figure(figsize=(15,7))\n",
      "#..\n",
      "ax1 = fig.add_subplot(131)\n",
      "p1 = ax1.pcolormesh(d_bin,zc,rain_bins.T,vmin=0,vmax=5)\n",
      "#p1 = ax1.pcolormesh(m_bin,zc,rain_bins.T,vmin=0,vmax=5)\n",
      "ax1.set_ylim(zmin,zmax)\n",
      "plt.colorbar(p1)\n",
      "#..\n",
      "ax2 = fig.add_subplot(132)\n",
      "p2 = ax2.pcolormesh(d_bin,zc,ice_bins.T,vmin=0,vmax=10)\n",
      "#p2 = ax1.pcolormesh(m_bin,zc,ice_bins.T,vmin=0,vmax=10)\n",
      "ax2.set_ylim(zmin,zmax)\n",
      "plt.colorbar(p2)\n",
      "#..\n",
      "ax3 = fig.add_subplot(133)\n",
      "p3 = ax3.pcolormesh(d_bin,zc,rain_bins.T+ice_bins.T,vmin=0,vmax=10)\n",
      "#p2 = ax1.pcolormesh(m_bin,zc,ice_bins.T,vmin=0,vmax=10)\n",
      "ax3.set_ylim(zmin,zmax)\n",
      "plt.colorbar(p3)\n",
      "#.."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 141,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0x7fa63716c320>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZgDs4lU0HDx7UNddco/nz52vs2LH627/9W506dSoORwjAjZxsO9mRT1yRAzwi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\nMhkO5IiIiIiIiEyGAzkiIiIiIiKT+T9MEo5M3NBjtAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fa6375c4750>"
       ]
      }
     ],
     "prompt_number": 141
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(ice_bins[:,25])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 142,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7fa6374a0150>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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gogKeecbqt6fq0rlRL5p18OBBRo4c2f7rESNG8Oabb8akKJFYMU2rv3roUMfX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\niMMpyEVEHE5BLiLicP8PyPfj6NeLny4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fa638526210>"
       ]
      }
     ],
     "prompt_number": 142
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(8,5))\n",
      "ax = fig.add_subplot(111)\n",
      "plt.scatter(d_bin,10.*np.log10(ice_bins[:,25]*d_bin**6))\n",
      "ax.set_ylim(-20,50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:3: RuntimeWarning: divide by zero encountered in log10\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 149,
       "text": [
        "(-20, 50)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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hpu6HHbWHfZDax360h32Q2A87aQ/70FjNLmaXyyUfn8tvHzFihE6fPi1J2rhx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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fa636e2ec90>"
       ]
      }
     ],
     "prompt_number": 149
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(8,5))\n",
      "ax = fig.add_subplot(111)\n",
      "plt.scatter(d_bin,10.*np.log10(rain_bins[:,15]*d_bin**6))\n",
      "ax.set_ylim(-20,50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 150,
       "text": [
        "(-20, 50)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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88spv1batXS1bhmvixCmqrKw0OxIANDqKGT5h1apV+tnPfq1z51y6ePGwVqw4renTnzE7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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fa636d83d90>"
       ]
      }
     ],
     "prompt_number": 150
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print ice_bins[:,25]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[  0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
        "   0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
        "   0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
        "   0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
        "   0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
        "   0.00000000e+00   0.00000000e+00   1.91852748e-02   2.99116168e-02\n",
        "   6.31376281e-02   2.69755274e-01   1.22347379e+00   6.60761404e+00\n",
        "   3.47782745e+01   5.85541496e+01   7.58642120e+01   7.90740356e+01\n",
        "   7.45549927e+01   6.55464325e+01   5.25843201e+01   3.93485870e+01\n",
        "   2.77075539e+01   1.85090847e+01   1.17980042e+01   7.21785688e+00\n",
        "   4.27128458e+00   2.47074294e+00   1.40988898e+00   7.93462515e-01\n",
        "   4.30224359e-01   2.14052483e-01   9.93235260e-02   4.60740738e-02\n",
        "   2.18051020e-02   6.49119020e-02]\n"
       ]
      }
     ],
     "prompt_number": 135
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print rain_bins[:,10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[  6.63915336e-01   6.64167047e-01   6.64433777e-01   6.64904952e-01\n",
        "   6.65619314e-01   6.66645169e-01   6.68093979e-01   6.70135736e-01\n",
        "   6.73021734e-01   6.77116334e-01   6.82937324e-01   6.91215038e-01\n",
        "   7.02970862e-01   7.19627500e-01   7.43150651e-01   7.76245058e-01\n",
        "   8.22609484e-01   8.87262523e-01   9.76932228e-01   1.10047376e+00\n",
        "   1.26211274e+00   1.44865704e+00   1.70764482e+00   2.03878164e+00\n",
        "   2.43974614e+00   2.86453032e+00   3.24542093e+00   3.51483583e+00\n",
        "   3.98263001e+00   5.10568762e+00   5.86756754e+00   5.58606577e+00\n",
        "   7.26478148e+00   5.01387787e+00   5.92011976e+00   4.28934956e+00\n",
        "   3.68117762e+00   2.60936451e+00   1.75892723e+00   1.10515106e+00\n",
        "   6.65508986e-01   3.90438884e-01   2.28429183e-01   1.34330094e-01\n",
        "   7.86304325e-02   4.34851162e-02   2.21749861e-02   1.04686217e-02\n",
        "   6.27009803e-03   2.00484395e-02]\n"
       ]
      }
     ],
     "prompt_number": 136
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}